Publications

Working Paper

  • [PDF] Eva Bartz, Martin Zaefferer, Olaf Mersmann, and Thomas Bartz-Beielstein. Experimental investigation and evaluation of model-based hyperparameter optimization. July Working Paper. Revision 1
    [Bibtex]
    @misc{bart21h,
    author = {Bartz, Eva and Zaefferer, Martin and Mersmann, Olaf and Bartz-Beielstein, Thomas},
    date-added = {2021-07-01 11:23:52 +0200},
    date-modified = {2021-07-21 15:18:45 +0200},
    keywords = {bartzPublic},
    month = {July},
    note = {Revision 1},
    title = {Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization},
    year = {Working Paper}}
  • [PDF] Thomas Bartz-Beielstein, Frederik Rehbach, Amrita Sen, and Martin Zaefferer. Surrogate model based hyperparameter tuning for deep learning with SPOT. July Working Paper. Revision 4
    [Bibtex]
    @misc{Bart21g,
    author = {Thomas Bartz-Beielstein and Frederik Rehbach and Amrita Sen and Martin Zaefferer},
    date-added = {2021-05-16 13:11:23 +0200},
    date-modified = {2021-07-21 15:19:15 +0200},
    institution = {{TH K{\"o}ln}},
    keywords = {bartzPublic},
    month = {July},
    note = {Revision 4},
    title = {Surrogate Model Based Hyperparameter Tuning for Deep Learning with {SPOT}},
    year = {Working Paper}}

2022

  • Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors. Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide. Springer, 2022. in print
    [Bibtex]
    @book{bart21i,
    date-added = {2022-07-19 11:46:33 +0200},
    date-modified = {2022-07-19 11:53:00 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    publisher = {Springer},
    title = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    year = {2022}}
  • Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach, and Martin Zaefferer. Case study i: tuning random forest. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 8, pages 177-201. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic6,
    author = {Bartz-Beielstein, Thomas and Chandrasekaran, Sowmya and Rehbach, Frederik and Zaefferer, Martin},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {8},
    date-added = {2022-07-19 12:01:31 +0200},
    date-modified = {2022-07-19 12:03:06 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {177-201},
    publisher = {Springer},
    title = {Case Study I: Tuning Random Forest},
    year = {2022}}
  • Thomas Bartz-Beielstein, Sowmya Chandrasekaran, and Frederik Rehbach. Case study ii: tuning of gradient boosting. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 9, pages 211-226. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic7,
    author = {Bartz-Beielstein, Thomas and Chandrasekaran, Sowmya and Rehbach, Frederik},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {9},
    date-added = {2022-07-19 12:03:12 +0200},
    date-modified = {2022-07-19 12:05:08 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {211-226},
    publisher = {Springer},
    title = {Case Study II: Tuning of Gradient Boosting},
    year = {2022}}
  • Thomas Bartz-Beielstein, Sowmya Chandrasekaran, and Frederik Rehbach. Case study iii: tuning of deep neural networks. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 10, pages 227-262. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic8,
    author = {Bartz-Beielstein, Thomas and Chandrasekaran, Sowmya and Rehbach, Frederik},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {10},
    date-added = {2022-07-19 12:03:54 +0200},
    date-modified = {2022-07-19 12:04:52 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {227-262},
    publisher = {Springer},
    title = {Case Study III: Tuning of Deep Neural Networks},
    year = {2022}}
  • Thomas Bartz-Beielstein. Hyperparameter tuning and optimization applications. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 6, pages 159-168. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic5,
    author = {Bartz-Beielstein, Thomas},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {6},
    date-added = {2022-07-19 12:00:34 +0200},
    date-modified = {2022-07-19 14:13:04 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {159-168},
    publisher = {Springer},
    title = {Hyperparameter Tuning and Optimization Applications},
    year = {2022}}
  • Thomas Bartz-Beielstein and Martin Zaefferer. Hyperparameter tuning approaches. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 4, pages 67-114. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic3,
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {4},
    date-added = {2022-07-19 11:57:15 +0200},
    date-modified = {2022-07-19 11:58:48 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {67-114},
    publisher = {Springer},
    title = {Hyperparameter Tuning Approaches},
    year = {2022}}
  • Thomas Bartz-Beielstein and Martin Zaefferer. Models. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 3, pages 27-66. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic2,
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {3},
    date-added = {2022-07-19 12:16:32 +0200},
    date-modified = {2022-07-19 12:16:59 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {27-66},
    publisher = {Springer},
    title = {Models},
    year = {2022}}
  • Thomas Bartz-Beielstein, Olaf Mersmann, and Sowmya Chandrasekaran. Ranking and result aggregation. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 5, pages 115-16. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic4,
    author = {Bartz-Beielstein, Thomas and Mersmann, Olaf and Chandrasekaran, Sowmya},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {5},
    date-added = {2022-07-19 11:59:17 +0200},
    date-modified = {2022-07-19 12:00:17 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {115-16},
    publisher = {Springer},
    title = {Ranking and Result Aggregation},
    year = {2022}}
  • Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann. Tuning: methodology. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 2, pages 7-26. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic1,
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {2},
    date-added = {2022-07-19 11:53:55 +0200},
    date-modified = {2022-07-19 11:55:02 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {7-26},
    publisher = {Springer},
    title = {Tuning: Methodology},
    year = {2022}}
  • [DOI] Margarita Rebolledo, Daan Zeeuwe, Thomas Bartz-Beielstein, and A. E. Eiben. Co-optimizing for task performance and energy efficiency in evolvable robots. Engineering applications of artificial intelligence, 113:104968, 2022.
    [Bibtex]
    @article{rebo22a,
    abstract = {Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots' morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots' controller parameters.},
    author = {Margarita Rebolledo and Daan Zeeuwe and Thomas Bartz-Beielstein and A.E. Eiben},
    date-added = {2022-07-19 13:48:14 +0200},
    date-modified = {2022-07-19 13:48:30 +0200},
    doi = {https://doi.org/10.1016/j.engappai.2022.104968},
    issn = {0952-1976},
    journal = {Engineering Applications of Artificial Intelligence},
    keywords = {Evolutionary robotics, Modular robots, Energy efficiency, Optimization, CPPN, Simulation, bartzPublic},
    pages = {104968},
    title = {Co-optimizing for task performance and energy efficiency in evolvable robots},
    url = {https://www.sciencedirect.com/science/article/pii/S0952197622001737},
    volume = {113},
    year = {2022},
    bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0952197622001737},
    bdsk-url-2 = {https://doi.org/10.1016/j.engappai.2022.104968}}
  • [DOI] Frederik Rehbach, Martin Zaefferer, Andreas Fischbach, Günter Rudolph, and Thomas Bartz-Beielstein. Benchmark-driven configuration of a parallel model-based optimization algorithm. Ieee transactions on evolutionary computation, pages 1-1, 2022.
    [Bibtex]
    @article{rehb21a,
    author = {Rehbach, Frederik and Zaefferer, Martin and Fischbach, Andreas and Rudolph, G{\"u}nter and Bartz-Beielstein, Thomas},
    date-added = {2022-07-19 13:50:13 +0200},
    date-modified = {2022-07-19 13:51:07 +0200},
    doi = {10.1109/TEVC.2022.3163843},
    journal = {IEEE Transactions on Evolutionary Computation},
    keywords = {bartzPublic},
    pages = {1-1},
    title = {Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm},
    year = {2022},
    bdsk-url-1 = {https://doi.org/10.1109/TEVC.2022.3163843},
    bdsk-file-1 = {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}}
  • [DOI] Aljoša Vodopija, Jörg Stork, Thomas Bartz-Beielstein, and Bogdan Filipič. Elevator group control as a constrained multiobjective optimization problem. Applied soft computing, 115:108277, 2022.
    [Bibtex]
    @article{vodo20a,
    abstract = {Modern elevator systems are controlled by the elevator group controllers that assign moving and stopping policies to the elevator cars. Designing an adequate elevator group control (EGC) policy is challenging for a number of reasons, one of them being conflicting optimization objectives. We address this task by formulating a corresponding constrained multiobjective optimization problem, and, in contrast to most studies in this domain, approach it using true multiobjective optimization methods capable of finding approximations for Pareto-optimal solutions. Specifically, we apply five multiobjective optimization algorithms with default constraint handling techniques and demonstrate their performance in optimizing EGC for nine elevator systems of various complexity. The experimental results confirm the scalability of the proposed methodology and suggest that NSGA-II equipped with the constrained-domination principle is the best performing algorithm on the test EGC systems. The proposed problem formulation and methodology allow for better understanding of the EGC design problem and provide insightful information to the stakeholders involved in deciding on elevator system configurations and control policies.},
    author = {Aljo{\v s}a Vodopija and J{\"o}rg Stork and Thomas Bartz-Beielstein and Bogdan Filipi{\v c}},
    date-added = {2022-07-19 10:49:02 +0200},
    date-modified = {2022-07-19 12:12:16 +0200},
    doi = {https://doi.org/10.1016/j.asoc.2021.108277},
    issn = {1568-4946},
    journal = {Applied Soft Computing},
    keywords = {Elevator group control, S-Ring model, Multiobjective optimization, NSGA-II, Pareto front approximation, bartzPublic},
    pages = {108277},
    title = {Elevator group control as a constrained multiobjective optimization problem},
    url = {https://www.sciencedirect.com/science/article/pii/S1568494621010899},
    volume = {115},
    year = {2022},
    bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S1568494621010899},
    bdsk-url-2 = {https://doi.org/10.1016/j.asoc.2021.108277},
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  • Martin Zaefferer, Olaf Mersmann, and Thomas Bartz-Beielstein. Global study: influence of tuning. In Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, editors, Hyperparameter Tuning for Machine and Deep Learning with R – A Practical Guide, chapter 12, pages 275-292. Springer, 2022. in print
    [Bibtex]
    @incollection{bart21ic9,
    author = {Zaefferer, Martin and Mersmann, Olaf and Bartz-Beielstein, Thomas},
    booktitle = {{Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide}},
    chapter = {12},
    date-added = {2022-07-19 12:06:30 +0200},
    date-modified = {2022-07-19 12:07:24 +0200},
    editor = {Bartz,Eva and Bartz-Beielstein, Thomas and Zaefferer, Martin and Mersmann, Olaf},
    isbn = {ISBN 978-981-19-5169-5},
    keywords = {bartzPublic},
    note = {in print},
    pages = {275-292},
    publisher = {Springer},
    title = {Global Study: Influence of Tuning},
    year = {2022}}

2021

  • [PDF] Eva Bartz, Martin Zaefferer, Olaf Mersmann, and Thomas Bartz-Beielstein. Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization. http://arxiv.org/abs/2107.08761, July 2021. Revision 1.
    [Bibtex]
    @misc{bart21harxiv,
    abstract = {Machine learning algorithms such as random forests or xgboost are gaining more importance and are increasingly incorporated into production processes in order to enable comprehensive digitization and, if possible, automation of processes. Hyperparameters of these algorithms used have to be set appropriately, which can be referred to as hyperparameter tuning or optimization. Based on the concept of tunability, this article presents an overview of theoretical and practical results for popular machine learning algorithms. This overview is accompanied by an experimental analysis of 30 hyperparameters from six relevant machine learning algorithms. In particular, it provides (i) a survey of important hyperparameters, (ii) two parameter tuning studies, and (iii) one extensive global parameter tuning study, as well as (iv) a new way, based on consensus ranking, to analyze results from multiple algorithms. The R package mlr is used as a uniform interface to the machine learning models. The R package SPOT is used to perform the actual tuning (optimization). All additional code is provided together with this paper.},
    author = {Eva Bartz and Martin Zaefferer and Olaf Mersmann and Thomas Bartz-Beielstein},
    date-added = {2021-07-20 12:01:24 +0200},
    date-modified = {2021-07-20 15:59:23 +0200},
    eprint = {arXiv:2107.08761},
    howpublished = {http://arxiv.org/abs/2107.08761},
    keywords = {bartzPublic},
    month = {July},
    mrclass = {68T07},
    note = {Revision 1.},
    title = {{E}xperimental {I}nvestigation and {E}valuation of {M}odel-based {H}yperparameter {O}ptimization},
    url = {http://arxiv.org/abs/2107.08761},
    year = {2021},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://arxiv.org/abs/2107.08761}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Tom Lawton, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, Amrita Sen, Aleksandr Subbotin, and Martin Zaefferer. Optimization and adaptation of a resource planning tool for hospitals under special consideration of the covid-19 pandemic. In 2021 ieee congress on evolutionary computation (cec), pages 728-735, 2021.
    [Bibtex]
    @inproceedings{bart21a,
    author = {Bartz-Beielstein, Thomas and Dr{\"o}scher, Marcel and G{\"u}r, Alpar and Hinterleitner, Alexander and Lawton, Tom and Mersmann, Olaf and Peeva, Dessislava and Reese, Lennard and Rehbach, Nicolas and Rehbach, Frederik and Sen, Amrita and Subbotin, Aleksandr and Zaefferer, Martin},
    booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)},
    date-added = {2021-08-15 21:16:37 +0200},
    date-modified = {2021-12-16 13:38:40 +0100},
    doi = {10.1109/CEC45853.2021.9504732},
    keywords = {bartzPublic, babsim},
    pages = {728-735},
    title = {Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic},
    year = {2021},
    bdsk-file-1 = {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},
    bdsk-url-1 = {https://doi.org/10.1109/CEC45853.2021.9504732}}
  • [PDF] [DOI] T. Bartz-Beielstein, M. Dröscher, A. Gür, A. Hinterleitner, O. Mersmann, D. Peeva, L. Reese, N. Rehbach, F. Rehbach, A. Sen, A. Subbotin, and M. Zaefferer. Resource planning for hospitals under special consideration of the covid-19 pandemic: optimization and sensitivity analysis. In Proceedings of the genetic and evolutionary computation conference companion, GECCO ’21, page 293–294, New York, NY, USA, 2021. Association for computing machinery.
    [Bibtex]
    @inproceedings{bart21f,
    abstract = {Pandemics pose a serious challenge to health-care institutions. To support the resource
    planning of health authorities from the Cologne region, BaBSim.Hospital, a tool for
    capacity planning based on discrete event simulation, was created. The predictive
    quality of the simulation is determined by 29 parameters with reasonable default values
    obtained in discussions with medical professionals. We aim to investigate and optimize
    these parameters to improve BaBSim.Hospital using a surrogate-based optimization approach
    and an in-depth sensitivity analysis.},
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, T. and Dr\"{o}scher, M. and G\"{u}r, A. and Hinterleitner, A. and Mersmann, O. and Peeva, D. and Reese, L. and Rehbach, N. and Rehbach, F. and Sen, A. and Subbotin, A. and Zaefferer, M.},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    date-added = {2021-07-31 15:04:49 +0200},
    date-modified = {2021-12-16 13:41:25 +0100},
    doi = {10.1145/3449726.3459473},
    isbn = {9781450383516},
    keywords = {bartzPublic, sensitivity analysis, COVID-19, simulation, capacity planning, surrogate models, optimization, hospital resource planning, bart21i, babsim},
    location = {Lille, France},
    numpages = {2},
    pages = {293--294},
    publisher = {Association for Computing Machinery},
    series = {GECCO '21},
    title = {Resource Planning for Hospitals under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis},
    url = {https://doi.org/10.1145/3449726.3459473},
    year = {2021},
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  • [PDF] Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, Amrita Sen, Aleksandr Subbotin, and Martin Zaefferer. Resource planning for hospitals under special consideration of the covid-19 pandemic: optimization and sensitivity analysis. arXiv, May 2021. http://arxiv.org/abs/2105.07420
    [Bibtex]
    @misc{bart21farxiv,
    abstract = {Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. They need to plan the resources required for handling the increased load, for instance, hospital beds and ventilators. To support the resource planning of local health authorities from the Cologne region, BaBSim.Hospital, a tool for capacity planning based on discrete event simulation, was created. The predictive quality of the simulation is determined by 29 parameters. Reasonable default values of these parameters were obtained in detailed discussions with medical professionals. We aim to investigate and optimize these parameters to improve BaBSim.Hospital. First approaches with "out-of-the-box" optimization algorithms failed. Implementing a surrogate-based optimization approach generated useful results in a reasonable time. To understand the behavior of the algorithm and to get valuable insights into the fitness landscape, an in-depth sensitivity analysis was performed. The sensitivity analysis is crucial for the optimization process because it allows focusing the optimization on the most important parameters. We illustrate how this reduces the problem dimension without compromising the resulting accuracy. The presented approach is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods.},
    author = {Thomas Bartz-Beielstein and Marcel Dr{\"o}scher and Alpar G{\"u}r and Alexander Hinterleitner and Olaf Mersmann and Dessislava Peeva and Lennard Reese and Nicolas Rehbach and Frederik Rehbach and Amrita Sen and Aleksandr Subbotin and Martin Zaefferer},
    date-added = {2021-05-18 22:51:42 +0200},
    date-modified = {2021-07-23 19:59:31 +0200},
    eprint = {arXiv:2105.07420},
    howpublished = {arXiv},
    keywords = {bartzPublic},
    month = {May},
    note = {http://arxiv.org/abs/2105.07420},
    title = {Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis},
    url = {http://arxiv.org/abs/2105.07420},
    year = {2021},
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    bdsk-url-1 = {http://arxiv.org/abs/2105.07420}}
  • [PDF] Thomas Bartz-Beielstein, Frederik Rehbach, Amrita Sen, and Martin Zaefferer. Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT. June 2021. Revision 3. http://arxiv.org/abs/2105.14625
    [Bibtex]
    @misc{bart21garxiv,
    abstract = {A surrogate model based hyperparameter tuning approach for deep learning is presented. This article demonstrates how the architecture-level parameters (hyperparameters) of deep learning models that were implemented in Keras/tensorflow can be optimized. The implementation of the tuning procedure is 100 % based on R, the software environment for statistical computing. With a few lines of code, existing R packages (tfruns and SPOT) can be combined to perform hyperparameter tuning. An elementary hyperparameter tuning task (neural network and the MNIST data) is used to exemplify this approach.},
    author = {Thomas Bartz-Beielstein and Frederik Rehbach and Amrita Sen and Martin Zaefferer},
    date-added = {2021-06-27 17:52:26 +0200},
    date-modified = {2021-07-20 14:13:36 +0200},
    eprint = {arXiv:2105.14625},
    keywords = {bartzPublic},
    month = {June},
    mrclass = {68T07},
    note = {Revision 3. http://arxiv.org/abs/2105.14625},
    title = {{S}urrogate {M}odel {B}ased {H}yperparameter {T}uning for {D}eep {L}earning with {S}{P}{O}{T}},
    url = {http://arxiv.org/abs/2105.14625},
    year = {2021},
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    bdsk-url-1 = {http://arxiv.org/abs/2105.14625}}
  • [DOI] Thomas Bartz-Beielstein, Frederik Rehbach, and Margarita Rebolledo. Tuning algorithms for black-box optimization: state of the art and future perspectives. In Panos Pardalos, Varvara Rasskazova, and Michael Vrahatis, editors, Black box optimization, machine learning and no-free lunch theorems, number 170 in Springer Optimization and Its Applications. Springer, 2021.
    [Bibtex]
    @incollection{Bart19g,
    author = {Bartz-Beielstein, Thomas and Rehbach, Frederik and Rebolledo, Margarita},
    booktitle = {Black Box Optimization, Machine Learning and No-Free Lunch Theorems},
    date-added = {2019-05-11 21:51:08 +0200},
    date-modified = {2021-02-11 10:45:50 +0100},
    doi = {https://doi.org/10.1007/978-3-030-66515-9_3},
    editor = {Panos Pardalos and Varvara Rasskazova and Michael Vrahatis},
    keywords = {bartzPublic},
    number = {170},
    publisher = {Springer},
    series = {Springer Optimization and Its Applications},
    title = {Tuning algorithms for black-box optimization: State of the art and future perspectives},
    year = {2021},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-66515-9_3}}
  • [PDF] [DOI] Lorenzo Gentile, Thomas Bartz-Beielstein, and Martin Zaefferer. Sequential parameter optimization for mixed-discrete problems, page 333–355. Springer international publishing, Cham, 2021.
    [Bibtex]
    @inbook{Gent18a,
    abstract = {Mixed-discrete optimization deals with mathematical optimization problems with multiple types of variables: discrete (nominal) taking values from a not-sortable set of possible elements, integer variables and variables taking values in a continuous domain. Mixed-discrete problems appear naturally in many contexts such as in the real world in the engineering domain, bioinformatics and data sciences, and this has led to an increased interest in the design of strong algorithms for different variants of the problem. Much effort has been spent over the last decades in studying and developing new methodologies, but unfortunately mixed-discrete optimization problems are much less understood then their ``non-mixed'' counterparts. In this chapter we will focus on the rather new approaches to handle mixed-discrete problems by means of surrogate methods.},
    address = {Cham},
    author = {Gentile, Lorenzo and Bartz-Beielstein, Thomas and Zaefferer, Martin},
    booktitle = {Optimization Under Uncertainty with Applications to Aerospace Engineering},
    date-added = {2021-07-20 13:43:14 +0200},
    date-modified = {2021-07-20 13:43:36 +0200},
    doi = {10.1007/978-3-030-60166-9_10},
    editor = {Vasile, Massimiliano},
    isbn = {978-3-030-60166-9},
    keywords = {bartzPublic},
    pages = {333--355},
    publisher = {Springer International Publishing},
    title = {Sequential Parameter Optimization for Mixed-Discrete Problems},
    url = {https://doi.org/10.1007/978-3-030-60166-9_10},
    year = {2021},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-60166-9_10}}
  • [DOI] Lorenzo Gentile, Cristian Greco, Edmondo Minisci, Thomas Bartz-Beielstein, and Massimiliano Vasile. Stochastic satellite tracking with constrained budget via structured-chromosome genetic algorithms. Optimization and engineering, 2021.
    [Bibtex]
    @article{gent21b,
    abstract = {This paper focuses on the scheduling under uncertainty of satellite tracking from a heterogeneous network of ground stations taking into account allocated resources. An optimisation-based approach is employed to efficiently select the optimal tracking schedule that minimises the final estimation uncertainty. Specifically, the scheduling is formulated as a variable-size problem, and a Structured-Chromosome Genetic Algorithm is developed to tackle the mixed-discrete global optimisation. The search algorithm employs genetic operators specifically revised to handle hierarchical search spaces. An orbit determination routine is run within each call to the fitness function to quantify the estimation uncertainty resulting from each candidate tracking schedule. The developed scheduler is tested on the tracking optimisation of a satellite in low Earth orbit, a highly perturbed dynamical regime. The obtained results show that the variable-size variants of Genetic Algorithms always outperform the fixed-size counterparts employed for comparison. In particular, Structured-Chromosome Genetic Algorithm is shown to find significantly better schedules under severely limited budgets.},
    author = {Gentile, Lorenzo and Greco, Cristian and Minisci, Edmondo and Bartz-Beielstein, Thomas and Vasile, Massimiliano},
    date = {2021/11/07},
    date-added = {2022-02-07 00:21:12 +0100},
    date-modified = {2022-02-07 00:21:42 +0100},
    doi = {10.1007/s11081-021-09693-1},
    id = {Gentile2021},
    isbn = {1573-2924},
    journal = {Optimization and Engineering},
    keywords = {bartzPublic},
    title = {Stochastic satellite tracking with constrained budget via structured-chromosome genetic algorithms},
    url = {https://doi.org/10.1007/s11081-021-09693-1},
    year = {2021},
    bdsk-url-1 = {https://doi.org/10.1007/s11081-021-09693-1}}
  • [PDF] Mike Preuss, Boris Naujoks, and Thomas Bartz-Beielstein. Benchmarking and Experimentation: Pitfalls and Best Practices. Tutorial CEC 2021. June 2021.
    [Bibtex]
    @misc{Preu21a,
    author = {Preuss, Mike and Naujoks, Boris and Bartz-Beielstein, Thomas},
    date-added = {2021-06-29 22:00:42 +0200},
    date-modified = {2021-06-29 22:02:15 +0200},
    keywords = {bartzPublic},
    month = {June},
    title = {{Benchmarking and Experimentation: Pitfalls and Best Practices. Tutorial CEC 2021}},
    year = {2021},
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  • [PDF] [DOI] M. Rebolledo, A. E. Eiben, and T. Bartz-Beielstein. Bayesian networks for mood prediction using unobtrusive ecological momentary assessments. In P. A. Castillo and J. L. {Jiménez Laredo}, editors, Applications of evolutionary computation – 24th international conference, evoapplications 2021, held as part of evostar 2021, proceedings, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), page 373–387. Springer science and business media deutschland gmbh, 2021. 24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021 ; Conference date: 07-04-2021 Through 09-04-2021
    [Bibtex]
    @inproceedings{rebo21a,
    abstract = {{\textcopyright} 2021, Springer Nature Switzerland AG.Depression affects an estimated 300 million people around the globe. Early detection of depression and associated mental health problems constitutes one of the best prevention methods when trying to reduce the disease{\textquoteright}s incidence. Information collected by tracking smartphone use behaviour and using ecological momentary assessments (EMA) can be used together with machine learning techniques to identify patterns indicative of depression and predict its appearance, contributing in this way to its early detection. However many of these techniques fail to identify the importance and relationships between the factors used to reach their prediction outcome. In this paper we propose the use of Bayesian networks (BN) as a tool to analyse and model data collected using EMA and smartphone measured behaviours. We compare the performance of BN against results obtained using support vector regression and random forest. The comparison is done in terms of efficacy, efficiency, and insight. Results show that no significant difference in efficacy was found between the models. However, BN presented clear advantages in terms of efficiency and insight given its probability factorization, graphical representation and ability to infer under uncertainty.},
    author = {M. Rebolledo and A.E. Eiben and T. Bartz-Beielstein},
    booktitle = {Applications of Evolutionary Computation - 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Proceedings},
    date-added = {2021-09-05 12:07:42 +0200},
    date-modified = {2021-09-05 12:07:59 +0200},
    doi = {10.1007/978-3-030-72699-7_24},
    editor = {P.A. Castillo and {Jim{\'e}nez Laredo}, J.L.},
    isbn = {9783030726980},
    keywords = {bartzPublic},
    language = {English},
    note = {24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021 ; Conference date: 07-04-2021 Through 09-04-2021},
    pages = {373--387},
    publisher = {Springer Science and Business Media Deutschland GmbH},
    series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    title = {Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments},
    year = {2021},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-72699-7_24}}
  • [PDF] [DOI] Margarita Rebolledo, Daan Zeeuwe, Thomas Bartz-Beielstein, and A. E. Eiben. Impact of energy efficiency on the morphology and behaviour of evolved robots. In Proceedings of the genetic and evolutionary computation conference companion, GECCO ’21, page 109–110, New York, NY, USA, 2021. Association for computing machinery.
    [Bibtex]
    @inproceedings{rebo21b,
    abstract = {Most evolutionary robotics studies focus on evolving some targeted behavior without
    considering energy usage. In this paper, we extend our simulator with a battery model
    to take energy consumption into account in a system where robot morphologies and controllers
    evolve simultaneously. The results show that including the energy consumption in the
    fitness in a multi-objective fashion reduces the average size of robot bodies while
    reducing their speed. However, robots generated without size reduction can achieve
    speeds comparable to robots from the baseline set.},
    address = {New York, NY, USA},
    author = {Rebolledo, Margarita and Zeeuwe, Daan and Bartz-Beielstein, Thomas and Eiben, A. E.},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    date-added = {2021-09-05 11:59:10 +0200},
    date-modified = {2021-09-05 12:00:18 +0200},
    doi = {10.1145/3449726.3459489},
    isbn = {9781450383516},
    keywords = {evolutionary robotics, modular robots, multi-objective evolution, energy efficiency, bartzPublic},
    location = {Lille, France},
    numpages = {2},
    pages = {109--110},
    publisher = {Association for Computing Machinery},
    series = {GECCO '21},
    title = {Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots},
    url = {https://doi.org/10.1145/3449726.3459489},
    year = {2021},
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    bdsk-url-1 = {https://doi.org/10.1145/3449726.3459489}}
  • [DOI] Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, Thomas Bartz-Beielstein, and A. E. Eiben. Behavior-based neuroevolutionary training in reinforcement learning. In Proceedings of the genetic and evolutionary computation conference companion, GECCO ’21, page 1753–1761, New York, NY, USA, 2021. Association for computing machinery.
    [Bibtex]
    @inproceedings{Stor20c,
    abstract = {In addition to their undisputed success in solving classical optimization problems,
    neuroevolutionary and population-based algorithms have become an alternative to standard
    reinforcement learning methods. However, evolutionary methods often lack the sample
    efficiency of standard value-based methods that leverage gathered state and value
    experience. If reinforcement learning for real-world problems with significant resource
    cost is considered, sample efficiency is essential. The enhancement of evolutionary
    algorithms with experience exploiting methods is thus desired and promises valuable
    insights. This work presents a hybrid algorithm that combines topology-changing neuroevolutionary
    optimization with value-based reinforcement learning. We illustrate how the behavior
    of policies can be used to create distance and loss functions, which benefit from
    stored experiences and calculated state values. They allow us to model behavior and
    perform a directed search in the behavior space by gradient-free evolutionary algorithms
    and surrogate-based optimization. For this purpose, we consolidate different methods
    to generate and optimize agent policies, creating a diverse population. We exemplify
    the performance of our algorithm on standard benchmarks and a purpose-built real-world
    problem. Our results indicate that combining methods can enhance the sample efficiency
    and learning speed for evolutionary approaches.},
    address = {New York, NY, USA},
    author = {Stork, J\"{o}rg and Zaefferer, Martin and Eisler, Nils and Tichelmann, Patrick and Bartz-Beielstein, Thomas and Eiben, A. E.},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    date-added = {2021-07-19 23:01:13 +0200},
    date-modified = {2021-07-19 23:06:21 +0200},
    doi = {10.1145/3449726.3463171},
    isbn = {9781450383516},
    keywords = {reinforcement learning, surrogate optimization, neural networks, neuroevolution, evolutionary algorithms, bartzPublic},
    location = {Lille, France},
    numpages = {9},
    pages = {1753--1761},
    publisher = {Association for Computing Machinery},
    series = {GECCO '21},
    title = {Behavior-Based Neuroevolutionary Training in Reinforcement Learning},
    url = {https://doi.org/10.1145/3449726.3463171},
    year = {2021},
    bdsk-url-1 = {https://doi.org/10.1145/3449726.3463171}}
  • [PDF] Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, Thomas Bartz-Beielstein, and A. E. Eiben. Behavior-based Neuroevolutionary Training in Reinforcement Learning. http://arxiv.org/abs/2105.07960, May 2021.
    [Bibtex]
    @misc{Stor20cArxiv,
    abstract = {In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinforcement learning methods. However, evolutionary methods often lack the sample efficiency of standard value-based methods that leverage gathered state and value experience. If reinforcement learning for real-world problems with significant resource cost is considered, sample efficiency is essential. The enhancement of evolutionary algorithms with experience exploiting methods is thus desired and promises valuable insights. This work presents a hybrid algorithm that combines topology-changing neuroevolutionary optimization with value-based reinforcement learning. We illustrate how the behavior of policies can be used to create distance and loss functions, which benefit from stored experiences and calculated state values. They allow us to model behavior and perform a directed search in the behavior space by gradient-free evolutionary algorithms and surrogate-based optimization. For this purpose, we consolidate different methods to generate and optimize agent policies, creating a diverse population. We exemplify the performance of our algorithm on standard benchmarks and a purpose-built real-world problem. Our results indicate that combining methods can enhance the sample efficiency and learning speed for evolutionary approaches.},
    author = {J{\"o}rg Stork and Martin Zaefferer and Nils Eisler and Patrick Tichelmann and Thomas Bartz-Beielstein and A. E. Eiben},
    date-added = {2021-05-18 22:48:21 +0200},
    date-modified = {2021-07-20 16:03:45 +0200},
    eprint = {arXiv:2105.07960},
    howpublished = {http://arxiv.org/abs/2105.07960},
    keywords = {bartzPublic},
    month = {May},
    title = {{B}ehavior-based {N}euroevolutionary {T}raining in {R}einforcement {L}earning},
    url = {http://arxiv.org/abs/2105.07960},
    year = {2021},
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    bdsk-url-1 = {http://arxiv.org/abs/2105.07960}}
  • [PDF] Jörg Stork, Philip Wenzel, Severin Landwein, Maria-Elena Algorri, Martin Zaefferer, Wolfgang Kusch, Martin Staubach, Thomas Bartz-Beielstein, Hartmut Köhn, Hermann Dejager, and Christian Wolf. Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs. Arxiv e-prints, pages arXiv:2107.13977, 07 2021. https://arxiv.org/abs/2107.13977
    [Bibtex]
    @article{stor21aarxiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210713977S},
    archiveprefix = {arXiv},
    author = {J{\"o}rg Stork and Philip Wenzel and Severin Landwein and Maria-Elena Algorri and Martin Zaefferer and Wolfgang Kusch and Martin Staubach and Thomas Bartz-Beielstein and Hartmut K{\"o}hn and Hermann Dejager and Christian Wolf},
    date-added = {2021-07-31 12:31:13 +0200},
    date-modified = {2021-07-31 12:33:54 +0200},
    eid = {arXiv:2107.13977},
    eprint = {2107.13977},
    journal = {arXiv e-prints},
    keywords = {bartzPublic, Computer Science - Artificial Intelligence},
    month = 07,
    note = {https://arxiv.org/abs/2107.13977},
    pages = {arXiv:2107.13977},
    primaryclass = {cs.AI},
    title = {{Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs}},
    year = 2021,
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://arxiv.org/abs/2107.13977}}
  • [DOI] Jan Strohschein, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. Cognitive capabilities for the caai in cyber-physical production systems. The international journal of advanced manufacturing technology, 115(11):3513–3532, 2021.
    [Bibtex]
    @article{stro21a,
    abstract = {This paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.},
    author = {Strohschein, Jan and Fischbach, Andreas and Bunte, Andreas and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas},
    date = {2021/08/01},
    date-added = {2022-02-07 00:23:52 +0100},
    date-modified = {2022-02-07 00:24:09 +0100},
    doi = {10.1007/s00170-021-07248-3},
    id = {Strohschein2021},
    isbn = {1433-3015},
    journal = {The International Journal of Advanced Manufacturing Technology},
    keywords = {bartzPublic},
    number = {11},
    pages = {3513--3532},
    title = {Cognitive capabilities for the CAAI in cyber-physical production systems},
    url = {https://doi.org/10.1007/s00170-021-07248-3},
    volume = {115},
    year = {2021},
    bdsk-url-1 = {https://doi.org/10.1007/s00170-021-07248-3}}
  • [DOI] Aljosa Vodopija, Jörg Stork, Thomas Bartz-Beielstein, and Bogdan Filipic. Elevator group control as a constrained multiobjective optimization problem. Applied soft computing, 2021.
    [Bibtex]
    @article{vodo20aOLD,
    author = {Aljosa Vodopija and J{\"o}rg Stork and Thomas Bartz-Beielstein and Bogdan Filipic},
    date-added = {2021-07-20 12:58:45 +0200},
    date-modified = {2022-07-19 10:49:27 +0200},
    doi = {https://doi.org/10.1016/j.asoc.2021.108277},
    journal = {Applied Soft Computing},
    keywords = {bartzPublic},
    title = {Elevator Group Control as a Constrained Multiobjective Optimization Problem},
    year = {2021},
    bdsk-file-1 = {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}}

2020

  • [DOI] Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, and El-Ghazali Talbi, editors. High-performance simulation-based optimization. Studies in Computational Intelligence. Springer, 2020.
    [Bibtex]
    @book{Bart18q,
    date-added = {2021-07-22 18:11:49 +0200},
    date-modified = {2021-07-22 18:11:49 +0200},
    doi = {10.1007/978-3-030-18764-4},
    editor = {Thomas Bartz-Beielstein and Bogdan Filipi\v{c} and Peter Koro\v{s}ec and El-Ghazali Talbi},
    keywords = {bartzPublic},
    publisher = {Springer},
    series = {Studies in Computational Intelligence},
    title = {High-Performance Simulation-Based Optimization},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-18764-4}}
  • [PDF] Eva Bartz, Thomas Bartz-Beielstein, Frederik Rehbach, Olaf Mersmann, Kaija Elvermann, Ralf Schmallenbach, Friedhelm Ortlieb, Sarah Leisner, Nikola Hahn, and Ralf Mühlenhaus. Einsatz künstlicher Intelligenz in der Bedarfsplanung im Gesundheitswesen, hier in der Bedarfsplanung von Intensivbetten im Pandemiefall. In Abstractbuch zum 20. Kongress der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin e.V.: Wissen schafft Vertrauen, pages 99-100. Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin e.V,, December 2020.
    [Bibtex]
    @inproceedings{bart20n,
    author = {Eva Bartz and Thomas Bartz-Beielstein and Frederik Rehbach and Olaf Mersmann and Kaija Elvermann and Ralf Schmallenbach and Friedhelm Ortlieb and Sarah Leisner and Nikola Hahn and Ralf M{\"u}hlenhaus},
    booktitle = {{Abstractbuch zum 20. Kongress der Deutschen Interdisziplin{\"a}ren Vereinigung f{\"u}r Intensiv- und Notfallmedizin e.V.: Wissen schafft Vertrauen}},
    date-added = {2021-07-22 18:11:07 +0200},
    date-modified = {2021-12-16 13:42:28 +0100},
    howpublished = {{Poster f{\"u}r den DIVI Kongress 2020}},
    keywords = {bartzPublic, babsim},
    month = {December},
    organization = {Deutsche Interdisziplin{\"a}re Vereinigung f{\"u}r Intensiv- und Notfallmedizin e.V},
    pages = {99-100},
    title = {{Einsatz k{\"u}nstlicher Intelligenz in der Bedarfsplanung im Gesundheitswesen, hier in der Bedarfsplanung von Intensivbetten im Pandemiefall}},
    year = {2020},
    bdsk-file-1 = {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}}
  • [PDF] Eva Bartz, Martin Zaefferer, Takeshi Katagiri, and Thomas Bartz-Beielstein. Architektur und Transport: Seillose, lineare Aufzüge und Künstliche Intelligenz. Transforming cities, 2:10-12, 2020.
    [Bibtex]
    @article{bart20l,
    author = {Bartz, Eva and Zaefferer, Martin and Katagiri, Takeshi and Bartz-Beielstein, Thomas},
    date-added = {2021-07-22 18:11:26 +0200},
    date-modified = {2021-07-22 18:11:26 +0200},
    journal = {Transforming Cities},
    keywords = {bartzPublic},
    pages = {10-12},
    title = {{Architektur und Transport: Seillose, lineare Aufz{\"u}ge und K{\"u}nstliche Intelligenz}},
    volume = {2},
    year = {2020},
    bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAgLi4vc2NpZWJvL1dlYnN0b3JlLmQvYmFydDIwbC5wZGZPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LYmFydDIwbC5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpiYXJ0MjBsLnBkZgAADgAYAAsAYgBhAHIAdAAyADAAbAAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL2JhcnQyMGwucGRmAAATAAEvAAAVAAIADP//AAAACAANABoAJABHAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAZc=}}
  • [PDF] Thomas Bartz-Beielstein, Eva Bartz, Frederik Rehbach, and Olaf Mersmann. Optimization of High-dimensional Simulation Models Using Synthetic Data. Arxiv e-prints, pages 1-10, 09 2020. https://arxiv.org/abs/2009.02781
    [Bibtex]
    @article{Bart20j,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200902781B},
    archiveprefix = {arXiv},
    author = {Bartz-Beielstein, Thomas and Bartz, Eva and Rehbach, Frederik and Mersmann, Olaf},
    date-added = {2021-07-22 18:09:23 +0200},
    date-modified = {2021-12-16 13:43:04 +0100},
    eid = {arXiv:2009.02781},
    eprint = {2009.02781},
    journal = {arXiv e-prints},
    keywords = {Statistics - Applications, Computer Science - Computers and Society, 68T20, I.2.1, J.3, I.2.6, I.2.8, J.2, K.4.1, K.4.0, bartzPublic, babsim},
    month = 09,
    note = {https://arxiv.org/abs/2009.02781},
    pages = {1-10},
    primaryclass = {stat.AP},
    title = {{Optimization of High-dimensional Simulation Models Using Synthetic Data}},
    year = 2020,
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  • Thomas Bartz-Beielstein, Frederik Rehbach, Olaf Mersmann, and Eva Bartz. Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic. December 2020. http://arxiv.org/abs/2012.07188
    [Bibtex]
    @misc{bart20uarxiv,
    abstract = {We present a resource-planning tool for hospitals under special consideration of the COVID-19 pandemic, called babsim.hospital. It provides many advantages for crisis teams, e.g., comparison with their own local planning, simulation of local events, simulation of several scenarios (worst / best case). There are benefits for medical professionals, e.g, analysis of the pandemic at local, regional, state and federal level, the consideration of special risk groups, tools for validating the length of stays and transition probabilities. Finally, there are potential advantages for administration, management, e.g., assessment of the situation of individual hospitals taking local events into account, consideration of relevant resources such as beds, ventilators, rooms, protective clothing, and personnel planning, e.g., medical and nursing staff. babsim.hospital combines simulation, optimization, statistics, and artificial intelligence processes in a very efficient way. The core is a discrete, event-based simulation model.},
    author = {Thomas Bartz-Beielstein and Frederik Rehbach and Olaf Mersmann and Eva Bartz},
    date-added = {2021-07-23 10:46:34 +0200},
    date-modified = {2021-12-16 13:42:05 +0100},
    eprint = {arXiv:2012.07188},
    keywords = {bartzPublic, babsim},
    month = {December},
    mrclass = {68T20},
    note = {http://arxiv.org/abs/2012.07188},
    title = {{H}ospital {C}apacity {P}lanning {U}sing {D}iscrete {E}vent {S}imulation {U}nder {S}pecial {C}onsideration of the {C}{O}{V}{I}{D}-19 {P}andemic},
    url = {http://arxiv.org/abs/2012.07188},
    year = {2020},
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  • [PDF] Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and Thomas Weise. Benchmarking in optimization: best practice and open issues. arXiv, 07 2020. https://arxiv.org/abs/2007.03488
    [Bibtex]
    @misc{bart20gArxiv,
    archiveprefix = {arXiv},
    author = {Thomas Bartz-Beielstein and Carola Doerr and Jakob Bossek and Sowmya Chandrasekaran and Tome Eftimov and Andreas Fischbach and Pascal Kerschke and Manuel Lopez-Ibanez and Katherine M. Malan and Jason H. Moore and Boris Naujoks and Patryk Orzechowski and Vanessa Volz and Markus Wagner and Thomas Weise},
    date-added = {2021-07-22 18:10:03 +0200},
    date-modified = {2021-07-22 18:10:03 +0200},
    eprint = {2007.03488},
    howpublished = {arXiv},
    keywords = {bartzPublic},
    month = {07},
    note = {https://arxiv.org/abs/2007.03488},
    primaryclass = {cs.NE},
    title = {Benchmarking in Optimization: Best Practice and Open Issues},
    url = {https://arxiv.org/abs/2007.03488},
    year = {2020},
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    bdsk-url-1 = {https://arxiv.org/abs/2007.03488}}
  • [PDF] Thomas Bartz-Beielstein, Frederik Rehbach, Olaf Mersmann, and Eva Bartz. Hospital capacity planning using discrete event simulation: introduction. Vignette, The Comprehensive {R} Archive Network, 11 2020. https://cran.r-project.org/web/packages/babsim.hospital/vignettes/babsim-vignette-introduction.html
    [Bibtex]
    @techreport{bart20u,
    author = {Bartz-Beielstein, Thomas and Rehbach, Frederik and Mersmann, Olaf and Bartz, Eva},
    date-added = {2021-07-22 18:10:46 +0200},
    date-modified = {2021-12-16 13:42:37 +0100},
    institution = {The Comprehensive {R} Archive Network},
    keywords = {bartzPublic, babsim},
    lastchecked = {2021-02-03},
    month = {11},
    note = {https://cran.r-project.org/web/packages/babsim.hospital/vignettes/babsim-vignette-introduction.html},
    title = {Hospital Capacity Planning Using Discrete Event Simulation: Introduction},
    type = {Vignette},
    url = {https://cran.r-project.org/web/packages/babsim.hospital/vignettes/babsim-vignette-introduction.pdf},
    year = {2020},
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  • [PDF] Thomas Bartz-Beielstein, Frederik Rehbach, Olaf Mersmann, and Eva Bartz. Babsim.hospital: a discrete-event simulation model for a hospital resource planning problem. Reference manual, The Comprehensive {R} Archive Network, 12 2020. https://cran.r-project.org/web/packages/babsim.hospital/babsim.hospital.pdf
    [Bibtex]
    @techreport{bart20t,
    abstract = {Implements a discrete-event simulation model for a hospital resource planning problem. The project is motivated by the challenges faced by health care institutions in the current COVID-19 pandemic. It can be used by health departments to forecast demand for intensive care beds, ventilators, and staff resources. Our modelling approach is inspired by "A novel modelling technique to predict resource requirements in critical care - a case study" (Lawton and McCooe 2019) and combines two powerful technologies: (i) discrete event simulation using the 'simmer' package and (ii) model-based optimization using 'SPOT'. Ucar I, Smeets B, Azcorra A (2019) . Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) . Lawton T, McCooe M (2019) .},
    author = {Bartz-Beielstein, Thomas and Rehbach, Frederik and Mersmann, Olaf and Bartz, Eva},
    date-added = {2021-07-22 18:10:24 +0200},
    date-modified = {2021-12-16 13:38:20 +0100},
    institution = {The Comprehensive {R} Archive Network},
    keywords = {bartzPublic, babsim},
    lastchecked = {2021-02-03},
    month = {12},
    note = {https://cran.r-project.org/web/packages/babsim.hospital/babsim.hospital.pdf},
    title = {babsim.hospital: a discrete-event simulation model for a hospital resource planning problem},
    type = {Reference manual},
    url = {https://cran.r-project.org/web/packages/babsim.hospital/babsim.hospital.pdf},
    year = {2020},
    bdsk-url-1 = {https://cran.r-project.org/package=babsim.hospital},
    bdsk-url-2 = {https://cran.r-project.org/web/packages/babsim.hospital/babsim.hospital.pdf}}
  • [PDF] Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and Thomas Weise. Benchmarking in optimization: best practice and open issues. CIplus Report 2/2020, TH Köln, Technische Hochschule Köln, 07 2020. urn:nbn:de:hbz:832-cos4-9022
    [Bibtex]
    @techreport{bart20gcos,
    address = {Technische Hochschule K{\"o}ln},
    archiveprefix = {cos},
    author = {Thomas Bartz-Beielstein and Carola Doerr and Jakob Bossek and Sowmya Chandrasekaran and Tome Eftimov and Andreas Fischbach and Pascal Kerschke and Manuel Lopez-Ibanez and Katherine M. Malan and Jason H. Moore and Boris Naujoks and Patryk Orzechowski and Vanessa Volz and Markus Wagner and Thomas Weise},
    date-added = {2021-07-23 10:40:48 +0200},
    date-modified = {2021-07-25 21:50:14 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    month = {07},
    note = {urn:nbn:de:hbz:832-cos4-9022},
    number = {2/2020},
    primaryclass = {cs.NE},
    title = {Benchmarking in Optimization: Best Practice and Open Issues},
    type = {CIplus Report},
    year = {2020},
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  • Thomas Bartz-Beielstein and Martin Zaefferer. Big data is often just bad data. DigitalXChange, June 2020. https://www.youtube.com/watch?v=VSKaw69lF5k
    [Bibtex]
    @misc{Bart20f,
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin},
    date-added = {2021-07-22 18:09:42 +0200},
    date-modified = {2021-07-23 10:49:27 +0200},
    howpublished = {DigitalXChange},
    keywords = {bartzPublic},
    month = {June},
    note = {https://www.youtube.com/watch?v=VSKaw69lF5k},
    title = {Big Data is often just Bad Data},
    year = {2020}}
  • [PDF] Sowmya Chandrasekaran, Margarita Rebolledo, and Thomas Bartz-Beielstein. EventDetectR – An Open-Source Event Detection System. Arxiv e-prints, pages arXiv:2011.09833, nov 2020. https://arxiv.org/abs/2011.09833
    [Bibtex]
    @article{Chan20aarxiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201109833C},
    archiveprefix = {arXiv},
    author = {Chandrasekaran, Sowmya and Rebolledo, Margarita and Bartz-Beielstein, Thomas},
    date-added = {2021-02-21 22:07:41 +0100},
    date-modified = {2021-07-23 16:39:24 +0200},
    eid = {arXiv:2011.09833},
    eprint = {2011.09833},
    journal = {arXiv e-prints},
    keywords = {Statistics - Computation, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, bartzPublic},
    month = nov,
    note = {https://arxiv.org/abs/2011.09833},
    pages = {arXiv:2011.09833},
    primaryclass = {stat.CO},
    title = {{EventDetectR -- An Open-Source Event Detection System}},
    year = 2020,
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  • [PDF] Sowmya Chandrasekaran, Margarita Rebolledo, and Thomas Bartz-Beielstein. EventDetectR–-An Open-Source Event Detection System. CIplus Report 9/2020, TH Köln, 2020. https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9232
    [Bibtex]
    @techreport{chan20acos,
    author = {Sowmya Chandrasekaran and Margarita Rebolledo and Thomas Bartz-Beielstein},
    date-added = {2021-07-22 18:08:58 +0200},
    date-modified = {2021-07-23 16:47:42 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    note = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9232},
    number = {9/2020},
    title = {{EventDetectR---An Open-Source Event Detection System}},
    type = {CIplus Report},
    year = {2020},
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  • [PDF] [DOI] Andreas Fischbach, Jan Strohschein, Andreas Bunte, Jörg Stork, Heide Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. CAAI–-a cognitive architecture to introduce artificial intelligence in cyber-physical production systems. The international journal of advanced manufacturing technology, 111(1):609–626, 2020.
    [Bibtex]
    @article{fisc20a,
    abstract = {This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user's declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration.},
    author = {Fischbach, Andreas and Strohschein, Jan and Bunte, Andreas and Stork, J{\"o}rg and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas},
    da = {2020/11/01},
    date-added = {2021-07-22 18:08:07 +0200},
    date-modified = {2021-07-22 18:08:07 +0200},
    doi = {10.1007/s00170-020-06094-z},
    id = {Fischbach2020},
    isbn = {1433-3015},
    journal = {The International Journal of Advanced Manufacturing Technology},
    keywords = {bartzPublic},
    number = {1},
    pages = {609--626},
    title = {{CAAI---a cognitive architecture to introduce artificial intelligence in cyber-physical production systems}},
    ty = {JOUR},
    url = {https://doi.org/10.1007/s00170-020-06094-z},
    volume = {111},
    year = {2020},
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  • [PDF] Andreas Fischbach, Jan Strohschein, Andreas Bunte, Jörg Stork, Heide Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. CAAI – A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems. Schriftenreihe CIplus 1/2020, TH Köln, 2020. https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-8834
    [Bibtex]
    @techreport{Fisc20acos,
    abstract = {This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case.},
    author = {Andreas Fischbach and Jan Strohschein and Andreas Bunte and J{\"o}rg Stork and Heide Faeskorn-Woyke and Natalia Moriz and Thomas Bartz-Beielstein},
    date-added = {2021-07-22 18:08:31 +0200},
    date-modified = {2021-07-23 16:49:37 +0200},
    howpublished = {Cologne Open Science},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    note = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-8834},
    number = {1/2020},
    title = {{C}{A}{A}{I} -- {A} {C}ognitive {A}rchitecture to {I}ntroduce {A}rtificial {I}ntelligence in {C}yber-{P}hysical {P}roduction {S}ystems},
    type = {Schriftenreihe CIplus},
    year = {2020},
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  • [DOI] Andreas Fischbach and Thomas Bartz-Beielstein. Improving the reliability of test functions generators. Applied soft computing, 92:106315, 2020.
    [Bibtex]
    @article{Fisc18a,
    abstract = {Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the performance of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments.},
    author = {Andreas Fischbach and Thomas Bartz-Beielstein},
    date-added = {2021-07-22 18:07:33 +0200},
    date-modified = {2021-07-22 18:07:33 +0200},
    doi = {https://doi.org/10.1016/j.asoc.2020.106315},
    issn = {1568-4946},
    journal = {Applied Soft Computing},
    keywords = {bartzPublic, Benchmarking, Continuous optimization, Test function selection, Design of experiments, Statistical analysis, ANOVA},
    pages = {106315},
    title = {Improving the reliability of test functions generators},
    url = {http://www.sciencedirect.com/science/article/pii/S1568494620302556},
    volume = {92},
    year = {2020},
    bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S1568494620302556},
    bdsk-url-2 = {https://doi.org/10.1016/j.asoc.2020.106315}}
  • [PDF] [DOI] Lorenzo Gentile, Elisa Morales, Domenico Quagliarella, Edmondo Minisci, Thomas Bartz-Beielstein, and Renato Tognaccini. High-lift devices topology optimisation using structured-chromosome genetic algorithm. In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1366-1374, July 2020.
    [Bibtex]
    @inproceedings{Gent20a,
    abstract = {This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process.},
    author = {Lorenzo Gentile and Elisa Morales and Domenico Quagliarella and Edmondo Minisci and Thomas Bartz-Beielstein and Renato Tognaccini},
    booktitle = {{2020 IEEE Congress on Evolutionary Computation (CEC)}},
    date-added = {2021-07-22 18:06:41 +0200},
    date-modified = {2021-07-23 16:06:44 +0200},
    doi = {10.1109/CEC48606.2020.9185603},
    keywords = {bartzPublic, genetic algorithms;optimisation;search problems;High-lift devices topology optimisation;High-Lift Devices configuration;decision variable;automatic optimisation tool;estimation routine;optimisation algorithm;SU2 flow;Structured-Chromosome Genetic Algorithm optimiser;SCGA;standard fixed-size continuous optimisation algorithms;configurational decisions;search algorithm;revised genetic operators;hierarchical search spaces;specialised optimisation;complete HLD design;multidisciplinary design optimisation;Optimization;Automotive components;Aerodynamics;Genetic algorithms;Task analysis;Biological cells;Standards;Aerospace engineering;High-lift devices;Optimisation;Genetic Algorithm;Mixed-variable;Variable-size},
    month = {July},
    pages = {1366-1374},
    title = {High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1109/CEC48606.2020.9185603}}
  • [PDF] [DOI] Lorenzo Gentile, Gianluca Filippi, Edmondo Minisci, Thomas Bartz-Beielstein, and Massimiliano Vasile. Preliminary spacecraft design by means of structured-chromosome genetic algorithms. In 2020 IEEE congress on evolutionary computation (CEC), pages 2107-2114, July 2020.
    [Bibtex]
    @inproceedings{Gent20b,
    abstract = {This paper presents a new methodology for complex system design by means of optimisation techniques. Within the Model-based Engineering approach, optimisation algorithms are used to explore optimal solutions of highly coupled and nonlinear systems. In such scenario, the optimal technology has to be identified and its settings have to be optimised. Relying on optimisation strategies for both the challenges brings to complex mixed-variable problem formulations involving continuous, integer and categorical parameters. Furthermore, part of the parameters are required only if certain technologies are adopted, bringing to variable-size formulations that standard optimisers cannot manage. Therefore, the proposed methodology relies on the use of variable-size mixed-variable global optimiser Structured-Chromosome Genetic Algorithm (SCGA). The advantages of this new method are shown by applying it for solving a space system preliminary design. In particular, two variants have been implemented distinguished by two different levels of complexity. To better appreciate the proposed approach, the same problems have been reformulated to be treated by a well known and appreciated optimiser in the field of spacecraft design, Multi- Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA). The final results of the two approaches are compared and commented.},
    author = {Gentile, Lorenzo and Filippi, Gianluca and Minisci, Edmondo and Bartz-Beielstein, Thomas and Vasile, Massimiliano},
    booktitle = {2020 {IEEE} Congress on Evolutionary Computation ({CEC})},
    date-added = {2021-07-22 18:07:10 +0200},
    date-modified = {2021-07-23 16:04:44 +0200},
    doi = {10.1109/CEC48606.2020.9185796},
    keywords = {bartzPublic},
    month = {July},
    pages = {2107-2114},
    title = {Preliminary spacecraft design by means of Structured-Chromosome Genetic Algorithms},
    url = {https://pure.strath.ac.uk/ws/portalfiles/portal/111822887/Gentile_etal_IEEE_WCCI_2020_Preliminary_spacecraft_design_by_means_of_Structured_chromosome.pdf},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1109/CEC48606.2020.9185796},
    bdsk-url-2 = {https://pure.strath.ac.uk/ws/portalfiles/portal/111822887/Gentile_etal_IEEE_WCCI_2020_Preliminary_spacecraft_design_by_means_of_Structured_chromosome.pdf}}
  • [PDF] Tom Peetz, Sebastian Vogt, Martin Zaefferer, and Thomas Bartz-Beielstein. Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools. Arxiv e-prints, pages arXiv:2009.01696, sep 2020. https://arxiv.org/abs/2009.01696
    [Bibtex]
    @article{Peet20a,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200901696P},
    archiveprefix = {arXiv},
    author = {Peetz, Tom and Vogt, Sebastian and Zaefferer, Martin and Bartz-Beielstein, Thomas},
    date-added = {2021-07-22 18:06:17 +0200},
    date-modified = {2021-07-22 18:06:17 +0200},
    eid = {arXiv:2009.01696},
    eprint = {2009.01696},
    journal = {arXiv e-prints},
    keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, bartzPublic},
    month = sep,
    note = {https://arxiv.org/abs/2009.01696},
    pages = {arXiv:2009.01696},
    primaryclass = {stat.ML},
    title = {{Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools}},
    year = 2020,
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Margarita Rebolledo, Ruxandra Stoean, A. E. Eiben, and Thomas Bartz-Beielstein. Hybrid variable selection and support vector regression for gas sensor optimization. In Bogdan Filipič, Edmondo Minisci, and Massimiliano Vasile, editors, Bioinspired optimization methods and their applications, Lecture Notes in Computer Science 12438, page 281–293, Cham, 2020. Springer international publishing.
    [Bibtex]
    @inproceedings{Rebo20c,
    abstract = {The improvement of combustion processes in industry, especially in the automotive branch, is of great importance to maintain the environmental permitted limits. Carbon monoxide concentration in the exhaust gases can give an insight into the efficiency of the combustion taking place and for this reason, it is important to have sensors that can measure it accurately. First results of a long term study with one of the leading sensor manufactures showed high performance using genetic programming. However, this expensive approach is difficult to apply in real-world settings. Therefore a hybrid optimization that combines support vector regression (SVR) with variable pre-selection is proposed. Three different methods for variable selection are compared for this application, a genetic algorithm, and two methods from Bayesian statistics: statistical equivalent signatures and projection predictive variable selection. Furthermore, a multi-objective approach using the same hybrid definition is implemented for the cases in which several sensors need to be considered simultaneously. Our results show that the hybrid model is an improvement compared to the previous study, while delivering good performance when dealing with a multivariate formulation. Genetic algorithms in combination with SVR lead to enhanced variation on the groups of selected variables.},
    address = {Cham},
    author = {Rebolledo, Margarita and Stoean, Ruxandra and Eiben, A. E. and Bartz-Beielstein, Thomas},
    booktitle = {Bioinspired Optimization Methods and Their Applications},
    date-added = {2021-07-22 18:05:43 +0200},
    date-modified = {2021-07-23 16:23:57 +0200},
    doi = {10.1007/978-3-030-63710-1_22},
    editor = {Filipi{\v{c}}, Bogdan and Minisci, Edmondo and Vasile, Massimiliano},
    isbn = {978-3-030-63710-1},
    keywords = {bartzPublic},
    pages = {281--293},
    publisher = {Springer International Publishing},
    series = {Lecture Notes in Computer Science 12438},
    title = {Hybrid Variable Selection and Support Vector Regression for Gas Sensor Optimization},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-63710-1_22}}
  • [PDF] Margarita Rebolledo, Sowmya Chandrasekaran, and Thomas Bartz-Beielstein. Technical Report: Flushing Strategies in Drinking Water Systems. Arxiv e-prints, pages arXiv:2012.13574, 12 2020. https://arxiv.org/abs/2012.13574
    [Bibtex]
    @article{Rebo20darxiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201213574R},
    archiveprefix = {arXiv},
    author = {Rebolledo, Margarita and Chandrasekaran, Sowmya and Bartz-Beielstein, Thomas},
    date-added = {2021-07-22 18:04:02 +0200},
    date-modified = {2021-07-25 21:38:55 +0200},
    eid = {arXiv:2012.13574},
    eprint = {2012.13574},
    journal = {arXiv e-prints},
    keywords = {Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, bartzPublic},
    month = 12,
    note = {https://arxiv.org/abs/2012.13574},
    pages = {arXiv:2012.13574},
    primaryclass = {cs.AI},
    title = {{Technical Report: Flushing Strategies in Drinking Water Systems}},
    year = 2020,
    bdsk-file-1 = {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}}
  • [PDF] Margarita Rebolledo, Sowmya Chandrasekaran, and Thomas Bartz-Beielstein. Sensor Placement for Contamination Detection in Water Distribution Systems. Arxiv e-prints, pages arXiv:2011.06406, nov 2020. https://arxiv.org/abs/2011.06406
    [Bibtex]
    @article{Rebo20earxiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201106406R},
    archiveprefix = {arXiv},
    author = {Rebolledo, Margarita and Chandrasekaran, Sowmya and Bartz-Beielstein, Thomas},
    date-added = {2021-07-22 18:05:17 +0200},
    date-modified = {2021-07-22 18:05:17 +0200},
    eid = {arXiv:2011.06406},
    eprint = {2011.06406},
    journal = {arXiv e-prints},
    keywords = {Electrical Engineering and Systems Science - Systems and Control, bartzPublic},
    month = nov,
    note = {https://arxiv.org/abs/2011.06406},
    pages = {arXiv:2011.06406},
    primaryclass = {eess.SY},
    title = {{Sensor Placement for Contamination Detection in Water Distribution Systems}},
    year = 2020,
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  • Margarita Rebolledo, Sowmya Chandrasekaran, and Thomas Bartz-Beielstein. Sensor Placement for Contamination Detection in Water Distribution Systems. CIplus Report 10/2020, TH Köln, 2020. https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9223
    [Bibtex]
    @techreport{Rebo20ecos,
    author = {Rebolledo, Margarita and Chandrasekaran, Sowmya and Bartz-Beielstein, Thomas},
    date-added = {2021-07-23 16:15:25 +0200},
    date-modified = {2021-07-23 16:17:16 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    note = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9223},
    number = {10/2020},
    title = {{Sensor Placement for Contamination Detection in Water Distribution Systems}},
    type = {CIplus Report},
    year = 2020,
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  • [DOI] Margarita Rebolledo, Frederik Rehbach, A. E. Eiben, and Thomas Bartz-Beielstein. Parallelized bayesian optimization for problems with expensive evaluation functions. In Proceedings of the 2020 genetic and evolutionary computation conference companion, GECCO ’20, page 231–232, New York, NY, USA, 2020. Association for computing machinery.
    [Bibtex]
    @inproceedings{Rebo20a,
    abstract = {Many black-box optimization problems rely on simulations to evaluate the quality of candidate solutions. These evaluations can be computationally expensive and very time-consuming. We present and approach to mitigate this problem by taking into consideration two factors: The number of evaluations and the execution time. We aim to keep the number of evaluations low by using Bayesian optimization (BO) - known to be sample efficient - and to reduce wall-clock times by executing parallel evaluations. Four parallelization methods using BO as optimizer are compared against the inherently parallel CMA-ES. Each method is evaluated on all the 24 objective functions of the Black-Box-Optimization-Benchmarking test suite in their 20-dimensional versions. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on most of the test functions, also on higher dimensions.},
    address = {New York, NY, USA},
    author = {Rebolledo, Margarita and Rehbach, Frederik and Eiben, A. E. and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion},
    date-added = {2021-07-22 18:04:34 +0200},
    date-modified = {2021-07-22 18:04:34 +0200},
    doi = {10.1145/3377929.3390017},
    isbn = {9781450371278},
    keywords = {CMAES, parallel optimization, bayesian optimization, BBOB, bartzPublic},
    location = {Canc\'{u}n, Mexico},
    numpages = {2},
    pages = {231--232},
    publisher = {Association for Computing Machinery},
    series = {GECCO '20},
    title = {Parallelized Bayesian Optimization for Problems with Expensive Evaluation Functions},
    url = {https://doi.org/10.1145/3377929.3390017},
    year = {2020},
    bdsk-url-1 = {https://doi.org/10.1145/3377929.3390017}}
  • [PDF] Margarita Rebolledo, Sowmya Chandrasekaran, and Thomas Bartz-Beielstein. Technical report: flushing strategies in drinking water systems. CIplus Report 11/20, TH Köln, 2020. http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9270
    [Bibtex]
    @techreport{Rebo20dcos,
    abstract = {Drinking water supply and distribution systems are critical infrastructure that has to be well maintained for the safety of the public. One important tool in the maintenance of water distribution systems (WDS) is flushing. Flushing is a process carried out in a periodic fashion to clean sediments and other contaminants in the water pipes. Given the different topographies, water composition and supply demand between WDS no single flushing strategy is suitable for all of them. In this report a non-exhaustive overview of optimization methods for flushing in WDS is given. Implementation of optimization methods for the flushing procedure and the flushing planing are presented. Suggestions are given as a possible option to optimise existing flushing planing frameworks.},
    author = {Margarita Rebolledo and Sowmya Chandrasekaran and Thomas Bartz-Beielstein},
    date-added = {2021-07-23 16:19:25 +0200},
    date-modified = {2021-07-23 16:21:49 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    note = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9270},
    number = {11/20},
    title = {Technical Report: Flushing Strategies in Drinking Water Systems},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9270},
    year = {2020},
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  • [PDF] [DOI] Margarita Rebolledo, Frederik Rehbach, A. E. Eiben, and Thomas Bartz-Beielstein. Parallelized bayesian optimization for expensive robot controller evolution. In Thomas Bäck, Mike Preuss, André Deutz, Michael Emmerich, Hao Wang, Carola Doerr, and Heike Trautmann, editors, Parallel problem solving from nature – ppsn xvi, volume 1 of Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), page 243–256. Springer science and business media deutschland gmbh, 2020. 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020
    [Bibtex]
    @inproceedings{rebo20b,
    abstract = {An important class of black-box optimization problems relies on using simulations to assess the quality of a given candidate solution. Solving such problems can be computationally expensive because each simulation is very time-consuming. We present an approach to mitigate this problem by distinguishing two factors of computational cost: the number of trials and the time needed to execute the trials. Our approach tries to keep down the number of trials by using Bayesian optimization (BO) --known to be sample efficient-- and reducing wall-clock times by parallel execution of trials. We compare the performance of four parallelization methods and two model-free alternatives. Each method is evaluated on all 24 objective functions of the Black-Box-Optimization-Benchmarking (BBOB) test suite in their five, ten, and 20-dimensional versions. Additionally, their performance is investigated on six test cases in robot learning. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on the BBOB test functions, especially for higher dimensions. On the robot learning tasks, the differences are less clear, but the data do support parallelized BO as the {\textquoteleft}best guess{\textquoteright}, winning on some cases and never losing.},
    author = {Margarita Rebolledo and Frederik Rehbach and Eiben, A. E. and Thomas Bartz-Beielstein},
    booktitle = {Parallel Problem Solving from Nature -- PPSN XVI},
    date-added = {2021-07-22 18:03:25 +0200},
    date-modified = {2021-07-23 16:54:21 +0200},
    doi = {10.1007/978-3-030-58112-1_17},
    editor = {Thomas B{\"a}ck and Mike Preuss and Andr{\'e} Deutz and Michael Emmerich and Hao Wang and Carola Doerr and Heike Trautmann},
    isbn = {9783030581114},
    keywords = {Bayesian optimization, BBOB benchmarking, CMA-ES, Parallelization, Robotics, bartzPublic},
    language = {English},
    note = {16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020},
    pages = {243--256},
    publisher = {Springer Science and Business Media Deutschland GmbH},
    series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    title = {Parallelized bayesian optimization for expensive robot controller evolution},
    volume = {1},
    year = {2020},
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  • [PDF] [DOI] Frederik Rehbach, Lorenzo Gentile, and Thomas Bartz-Beielstein. Variable reduction for surrogate-based optimization. In Proceedings of the 2020 genetic and evolutionary computation conference, GECCO ’20, page 1177–1185, New York, NY, USA, 2020. Association for computing machinery.
    [Bibtex]
    @inproceedings{Rehb20b,
    address = {New York, NY, USA},
    author = {Rehbach, Frederik and Gentile, Lorenzo and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
    date-added = {2021-07-22 18:02:05 +0200},
    date-modified = {2021-07-22 18:02:05 +0200},
    doi = {10.1145/3377930.3390195},
    isbn = {9781450371285},
    keywords = {bartzPublic, dimensionality reduction, surrogates, surrogate-based optimization, LASSO, real-world, modeling, bartzPublic},
    location = {Canc\'{u}n, Mexico},
    numpages = {9},
    pages = {1177--1185},
    publisher = {Association for Computing Machinery},
    series = {GECCO '20},
    title = {Variable Reduction for Surrogate-Based Optimization},
    url = {https://doi.org/10.1145/3377930.3390195},
    year = {2020},
    bdsk-url-1 = {https://doi.org/10.1145/3377930.3390195}}
  • [PDF] Frederik Rehbach, Lorenzo Gentile, and Thomas Bartz-Beielstein. Variable reduction for surrogate-based optimization. CIplus Report 5/2020, TH Köln, 2020. http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9068
    [Bibtex]
    @techreport{rehb20bcos,
    abstract = {Real-world problems such as computational fluid dynamics simulations and finite element analyses are computationally expensive. A standard approach to mitigating the high computational expense is Surrogate-Based Optimization (SBO). Yet, due to the high-dimensionality of many simulation problems, SBO is not directly applicable or not efficient. Reducing the dimensionality of the search space is one method to overcome this limitation. In addition to the applicability of SBO, dimensionality reduction enables easier data handling and improved data and model interpretability. Regularization is considered as one state-of-the-art technique for dimensionality reduction. We propose a hybridization approach called Regularized-Surrogate-Optimization (RSO) aimed at overcoming difficulties related to high-dimensionality. It couples standard Kriging-based SBO with regularization techniques. The employed regularization methods are based on three adaptations of the least absolute shrinkage and selection operator (LASSO). In addition, tree-based methods are analyzed as an alternative variable selection method. An extensive study is performed on a set of artificial test functions and two real-world applications: the electrostatic precipitator problem and a multilayered composite design problem. Experiments reveal that RSO requires significantly less time than standard SBO to obtain comparable results. The pros and cons of the RSO approach are discussed, and recommendations for practitioners are presented.},
    author = {Frederik Rehbach and Lorenzo Gentile and Thomas Bartz-Beielstein},
    date-added = {2021-07-23 16:26:43 +0200},
    date-modified = {2021-07-23 16:28:34 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    note = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9068},
    number = {5/2020},
    title = {Variable Reduction for Surrogate-Based Optimization},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9068},
    year = {2020},
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    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9068}}
  • [PDF] Frederik Rehbach, Lorenzo Gentile, and Thomas Bartz-Beielstein. Feature selection for surrogate model-based optimization. CIplus Report 3/2020, TH Köln, 2020.
    [Bibtex]
    @techreport{rehb18ccos,
    abstract = {We propose a hybridization approach called Regularized-Surrogate- Optimization (RSO) aimed at overcoming difficulties related to high- dimensionality. It combines standard Kriging-based SMBO with regularization techniques. The employed regularization methods use the least absolute shrinkage and selection operator (LASSO). An extensive study is performed on a set of artificial test functions and two real-world applications: the electrostatic precipitator problem and a multilayered composite design problem. Experiments reveal that RSO requires significantly less time than Kriging to obtain comparable results. The pros and cons of the RSO approach are discussed and recommendations for practitioners are presented.},
    author = {Frederik Rehbach and Lorenzo Gentile and Thomas Bartz-Beielstein},
    date-added = {2021-07-23 16:30:07 +0200},
    date-modified = {2021-07-23 16:31:01 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    number = {3/2020},
    title = {Feature Selection for Surrogate Model-Based Optimization},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9044},
    year = {2020},
    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9044}}
  • [PDF] Frederik Rehbach, Martin Zaefferer, Boris Naujoks, and Thomas Bartz-Beielstein. Expected improvement versus predicted value in surrogate-based optimization. CIplus Report 4/2020, TH Köln, 2020. http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9055
    [Bibtex]
    @techreport{rehb20acos,
    abstract = {Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next. When Kriging is used as the surrogate model of choice (also called Bayesian optimization), one of the most frequently chosen criteria is expected improvement. We argue that the popularity of expected improvement largely relies on its theoretical properties rather than empirically validated performance. Few results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of the surrogate model. We benchmark both infill criteria in an extensive empirical study on the `BBOB' function set. This investigation includes a detailed study of the impact of problem dimensionality on algorithm performance. The results support the hypothesis that exploration loses importance with increasing problem dimensionality. A statistical analysis reveals that the purely exploitative search with the predicted value criterion performs better on most problems of five or higher dimensions. Possible reasons for these results are discussed. In addition, we give an in-depth guide for choosing the infill criteria based on prior knowledge about the problem at hand, its dimensionality, and the available budget.},
    author = {Frederik Rehbach and Martin Zaefferer and Boris Naujoks and Thomas Bartz-Beielstein},
    date-added = {2021-07-23 16:33:11 +0200},
    date-modified = {2021-07-23 16:34:23 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    note = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9055},
    number = {4/2020},
    title = {Expected Improvement versus Predicted Value in Surrogate-Based Optimization},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9055},
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    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9055}}
  • [PDF] Frederik Rehbach, Martin Zaefferer, Boris Naujoks, and Thomas Bartz-Beielstein. Expected Improvement versus Predicted Value in Surrogate-Based Optimization. arXiv, Feb 2020. http://arxiv.org/abs/2001.02957
    [Bibtex]
    @misc{Rehb20aarxiv,
    abstract = {Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next. When Kriging is used as the surrogate model of choice (also called Bayesian optimization), one of the most frequently chosen criteria is expected improvement. We argue that the popularity of expected improvement largely relies on its theoretical properties rather than empirically validated performance. Few results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of the surrogate model. We benchmark both infill criteria in an extensive empirical study on the `BBOB' function set. This investigation includes a detailed study of the impact of problem dimensionality on algorithm performance. The results support the hypothesis that exploration loses importance with increasing problem dimensionality. A statistical analysis reveals that the purely exploitative search with the predicted value criterion performs better on most problems of five or higher dimensions. Possible reasons for these results are discussed. In addition, we give an in-depth guide for choosing the infill criteria based on prior knowledge about the problem at hand, its dimensionality, and the available budget.},
    author = {Frederik Rehbach and Martin Zaefferer and Boris Naujoks and Thomas Bartz-Beielstein},
    date-added = {2021-07-22 18:01:21 +0200},
    date-modified = {2021-07-23 16:35:14 +0200},
    eprint = {arXiv:2001.02957},
    howpublished = {arXiv},
    keywords = {bartzPublic},
    month = {Feb},
    note = {http://arxiv.org/abs/2001.02957},
    title = {{E}xpected {I}mprovement versus {P}redicted {V}alue in {S}urrogate-{B}ased {O}ptimization},
    url = {http://arxiv.org/abs/2001.02957},
    year = {2020},
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    bdsk-url-1 = {http://arxiv.org/abs/2001.02957}}
  • [PDF] [DOI] Frederik Rehbach, Martin Zaefferer, Boris Naujoks, and Thomas Bartz-Beielstein. Expected improvement versus predicted value in surrogate-based optimization. In Proceedings of the 2020 genetic and evolutionary computation conference, GECCO ’20, page 868–876, New York, NY, USA, 2020. Association for computing machinery.
    [Bibtex]
    @inproceedings{Rehb20a,
    address = {New York, NY, USA},
    author = {Rehbach, Frederik and Zaefferer, Martin and Naujoks, Boris and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
    date-added = {2021-07-22 18:00:47 +0200},
    date-modified = {2021-07-22 18:00:47 +0200},
    doi = {10.1145/3377930.3389816},
    isbn = {9781450371285},
    keywords = {bartzPublic, infill criterion, acquisition function, surrogate-based optimization, bayesian optimization},
    location = {Canc\'{u}n, Mexico},
    numpages = {9},
    pages = {868--876},
    publisher = {Association for Computing Machinery},
    series = {GECCO '20},
    title = {Expected Improvement versus Predicted Value in Surrogate-Based Optimization},
    url = {https://doi.org/10.1145/3377930.3389816},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1145/3377930.3389816}}
  • [DOI] Jörg Stork, Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, Andreas Fischbach, Beate Breiderhoff, Boris Naujoks, and Tea Tušar. Open issues in surrogate-assisted optimization, page 225–244. Springer international publishing, Cham, 2020.
    [Bibtex]
    @inbook{Nauj17a,
    abstract = {Surrogate-assisted optimization was developed for handling complex and costly problems, which arise from real-world applications. The main idea behind surrogate-assisted optimization is to optimally exhaust the available information to lower the amount of required expensive function evaluations thus saving time, resources and the related costs. This chapter outlines the existing challenges in this field that include benchmarking, constraint handling, constructing ensembles of surrogates and solving discrete and/or multi-objective optimization problems. We discuss shortcomings of existing techniques, propose suggestions for improvements and give an outlook on promising research directions. This is valuable for practitioners and researchers alike, since the increased availability of computational resources on the one hand and the continuous development of new approaches on the other hand raise many intricate new problems in this field.},
    address = {Cham},
    author = {Stork, J{\"o}rg and Friese, Martina and Zaefferer, Martin and Bartz-Beielstein, Thomas and Fischbach, Andreas and Breiderhoff, Beate and Naujoks, Boris and Tu{\v{s}}ar, Tea},
    booktitle = {High-Performance Simulation-Based Optimization},
    date-added = {2021-07-22 18:00:10 +0200},
    date-modified = {2021-07-22 18:00:10 +0200},
    doi = {10.1007/978-3-030-18764-4_10},
    editor = {Bartz-Beielstein, Thomas and Filipi{\v{c}}, Bogdan and Koro{\v{s}}ec, Peter and Talbi, El-Ghazali},
    isbn = {978-3-030-18764-4},
    keywords = {bartzPublic},
    pages = {225--244},
    publisher = {Springer International Publishing},
    title = {Open Issues in Surrogate-Assisted Optimization},
    url = {https://doi.org/10.1007/978-3-030-18764-4_10},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-18764-4_10}}
  • [PDF] [DOI] Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, and A. E. Eiben. Understanding the behavior of reinforcement learning agents. In Bogdan Filipič, Edmondo Minisci, and Massimiliano Vasile, editors, Bioinspired optimization methods and their applications, Lecture Notes in Computer Science 12438, page 148–160, Cham, 2020. Springer international publishing.
    [Bibtex]
    @inproceedings{Stor20a,
    abstract = {Reinforcement Learning (RL) is the process of training agents to solve specific tasks, based on measures of reward. Understanding the behavior of an agent in its environment can be crucial. For instance, if users understand why specific agents fail at a task, they might be able to define better reward functions, to steer the agents' development in the right direction. Understandability also empowers decisions for agent deployment. If we know why the controller of an autonomous car fails or excels in specific traffic situations, we can make better decisions on whether/when to use them in practice. We aim to facilitate the understandability of RL. To that end, we investigate and observe the behavioral space: the set of actions of an agent observed for a set of input states. Consecutively, we develop measures of distance or similarity in that space and analyze how agents compare in their behavior. Moreover, we investigate which states and actions are critical for a task, and determine the correlation between reward and behavior. We utilize two basic RL environments to investigate our measures. The results showcase the high potential of inspecting an agents' behavior and comparing their distance in behavior space.},
    address = {Cham},
    author = {Stork, J{\"o}rg and Zaefferer, Martin and Bartz-Beielstein, Thomas and Eiben, A. E.},
    booktitle = {Bioinspired Optimization Methods and Their Applications},
    date-added = {2021-07-22 17:59:26 +0200},
    date-modified = {2021-07-23 16:36:43 +0200},
    doi = {10.1007/978-3-030-63710-1_12},
    editor = {Filipi{\v{c}}, Bogdan and Minisci, Edmondo and Vasile, Massimiliano},
    isbn = {978-3-030-63710-1},
    keywords = {bartzPublic},
    pages = {148--160},
    publisher = {Springer International Publishing},
    series = {Lecture Notes in Computer Science 12438},
    title = {Understanding the Behavior of Reinforcement Learning Agents},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-63710-1_12}}
  • [PDF] [DOI] Jörg Stork, A. E. Eiben, and Thomas Bartz-Beielstein. A new taxonomy of global optimization algorithms. Natural computing, pages 1-24, 2020.
    [Bibtex]
    @article{Stor20b,
    abstract = {Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by automatic algorithm generation. The extracted features of algorithms, their main concepts, and search operators, allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms.},
    author = {Stork, J{\"o}rg and Eiben, A. E. and Bartz-Beielstein, Thomas},
    da = {2020/11/27},
    date-added = {2021-07-22 17:58:53 +0200},
    date-modified = {2021-07-22 17:58:53 +0200},
    doi = {10.1007/s11047-020-09820-4},
    id = {Stork2020},
    isbn = {1572-9796},
    journal = {Natural Computing},
    keywords = {bartzPublic},
    pages = {1-24},
    title = {A new taxonomy of global optimization algorithms},
    ty = {JOUR},
    url = {https://doi.org/10.1007/s11047-020-09820-4},
    year = {2020},
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    bdsk-url-1 = {https://doi.org/10.1007/s11047-020-09820-4}}
  • [PDF] Jan Strohschein, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems. Arxiv e-prints, pages arXiv:2012.01823, 2020. https://arxiv.org/abs/2012.01823
    [Bibtex]
    @article{Stro20aArXiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201201823S},
    archiveprefix = {arXiv},
    author = {Strohschein, Jan and Fischbach, Andreas and Bunte, Andreas and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas},
    date-added = {2021-07-22 17:58:10 +0200},
    date-modified = {2021-07-22 17:58:10 +0200},
    eid = {arXiv:2012.01823},
    eprint = {2012.01823},
    journal = {arXiv e-prints},
    keywords = {Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Software Engineering, bartzPublic},
    month = dec,
    note = {https://arxiv.org/abs/2012.01823},
    pages = {arXiv:2012.01823},
    primaryclass = {cs.AI},
    title = {{Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems}},
    year = 2020,
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  • [PDF] Aljosa Vodopija, Jörg Stork, Thomas Bartz-Beielstein, and Bogdan Filipic. Elevator group control as a constrained multiobjective optimization problem. June 2020. Preprint
    [Bibtex]
    @misc{vodo20aarxiv,
    abstract = {Modern elevator systems are controlled by the elevator group controllers that assign moving and stopping policies to the elevator cars. Designing an adequate elevator group control (EGC) policy is challenging for a number of reasons, one of them being conflicting optimization objectives. We address this task by formulating a corresponding constrained multiobjective optimization problem, and, in contrast to most studies in this domain, approach it using true multiobjective optimization methods capable of finding approximations for Pareto-optimal solutions. Specifically, we apply three multiobjective evolutionary algorithms with default constraint handling techniques and demonstrate their performance in optimizing EGC for nine elevator systems of various complexity. The experimental results confirm the scalability of the proposed methodology and suggest that NSGA-II equipped with the constrained-domination principle is the best performing algorithm on the test EGC systems. The proposed problem formulation and methodology allow for better understanding of the EGC design problem and provide insightful information to the stakeholders involved in deciding on elevator system configurations and control policies.},
    author = {Aljosa Vodopija and J{\"o}rg Stork and Thomas Bartz-Beielstein and Bogdan Filipic},
    date-added = {2021-07-22 17:57:07 +0200},
    date-modified = {2021-07-22 17:57:07 +0200},
    keywords = {bartzPublic},
    month = {June},
    note = {Preprint},
    title = {Elevator Group Control as a Constrained Multiobjective Optimization Problem},
    year = {2020},
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2019

  • [PDF] Thomas Bartz-Beielstein. Why we need an AI-resilient society. Preprint, arXiv, December 2019. http://arxiv.org/abs/1912.08786. For associated TEDx video, see https://youtu.be/f6c2ngp7rqY
    [Bibtex]
    @techreport{Bart19oarxiv,
    abstract = {Artificial intelligence is considered as a key technology. It has a huge impact on our society. Besides many positive effects, there are also some negative effects or threats.
    Some of these threats to society are well-known, e.g., weapons or killer robots. But there are also threats that are ignored. These unknown-knowns or blind spots affect privacy, and facilitate manipulation and mistaken identities.
    We cannot trust data, audio, video, and identities any more. Democracies are able to cope with known threats, the known-knowns.
    Transforming unknown-knowns to known-knowns is one important cornerstone of resilient societies. An AI-resilient society is able to transform threats caused by new AI
    tecchnologies such as generative adversarial networks. Resilience can be seen as a positive adaptation of these threats. We propose three strategies how this adaptation can be achieved:
    awareness, agreements, and red flags. This article accompanies the TEDx talk Why we urgently need an AI-resilient society, see https://youtu.be/f6c2ngp7rqY.},
    author = {Thomas Bartz-Beielstein},
    date-added = {2021-07-22 17:56:45 +0200},
    date-modified = {2021-07-22 17:56:45 +0200},
    eprint = {arXiv:1912.08786},
    institution = {arXiv},
    keywords = {aritifical intelligence, generative adversarial networks, bartzPublic},
    month = {December},
    note = {http://arxiv.org/abs/1912.08786. For associated TEDx video, see https://youtu.be/f6c2ngp7rqY},
    title = {{Why we need an AI-resilient society}},
    type = {Preprint},
    url = {http://arxiv.org/abs/1912.08786},
    year = {2019},
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    bdsk-url-1 = {http://arxiv.org/abs/1912.08786}}
  • [DOI] Andreas Bunte, Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, Heide Faeskorn-Woyke, and Oliver Niggemann. Evaluation of cognitive architectures for cyber-physical production systems. In 24th IEEE international conference on emerging technologies and factory automation, ETFA 2019, zaragoza, spain, september 10-13, 2019, page 729–736, 2019.
    [Bibtex]
    @inproceedings{Bunt19a,
    author = {Andreas Bunte and Andreas Fischbach and Jan Strohschein and Thomas Bartz-Beielstein and Heide Faeskorn-Woyke and Oliver Niggemann},
    biburl = {https://dblp.org/rec/bib/conf/etfa/BunteFSBFN19},
    booktitle = {24th {IEEE} International Conference on Emerging Technologies and Factory Automation, {ETFA} 2019, Zaragoza, Spain, September 10-13, 2019},
    date-added = {2019-11-29 22:22:52 +0100},
    date-modified = {2021-07-23 17:54:45 +0200},
    doi = {10.1109/ETFA.2019.8869038},
    keywords = {bartzPublic},
    pages = {729--736},
    timestamp = {Thu, 24 Oct 2019 15:51:18 +0200},
    title = {Evaluation of Cognitive Architectures for Cyber-Physical Production Systems},
    url = {https://doi.org/10.1109/ETFA.2019.8869038},
    year = {2019},
    bdsk-url-1 = {https://doi.org/10.1109/ETFA.2019.8869038}}
  • [PDF] Andreas Bunte, Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, Heide Faeskorn-Woyke, and Oliver Niggemann. Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. Arxiv e-prints, pages arXiv:1902.08448, Feb 2019.
    [Bibtex]
    @article{Bunt19aArxiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190208448B},
    archiveprefix = {arXiv},
    author = {Andreas Bunte and Andreas Fischbach and Jan Strohschein and Thomas Bartz-Beielstein and Heide Faeskorn-Woyke and Oliver Niggemann},
    date-added = {2019-03-07 21:15:40 +0100},
    date-modified = {2021-07-23 17:55:09 +0200},
    eid = {arXiv:1902.08448},
    eprint = {1902.08448},
    journal = {arXiv e-prints},
    keywords = {Computer Science - Computers and Society, bartzPublic, KIOpt},
    month = {Feb},
    pages = {arXiv:1902.08448},
    primaryclass = {cs.CY},
    title = {{Evaluation of Cognitive Architectures for Cyber-Physical Production Systems}},
    year = {2019},
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  • [PDF] Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, and Heide Faeskorn-Woyke. KOARCH – Kognitive Architektur für Cyber-Physische Produktionssysteme. In Digital-xchange 2019. TH Köln,, Februar 2019.
    [Bibtex]
    @inproceedings{Fisc19a,
    author = {Fischbach, Andreas and Strohschein, Jan and Bartz-Beielstein, Thomas and Faeskorn-Woyke, Heide},
    booktitle = {Digital-Xchange 2019},
    date-added = {2019-02-21 21:27:13 +0100},
    date-modified = {2021-07-23 19:45:11 +0200},
    keywords = {bartzPublic},
    month = {Februar},
    organization = {TH K{\"o}ln},
    title = {{KOARCH - Kognitive Architektur f{\"u}r Cyber-Physische Produktionssysteme}},
    year = {2019},
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  • [PDF] [DOI] Martina Friese, Thomas Bartz-Beielstein, Thomas Bäck, Boris Naujoks, and Michael Emmerich. Weighted ensembles in model-based global optimization. Aip conference proceedings, 2070(1):1-5, 2019.
    [Bibtex]
    @article{Frie18a,
    author = {Friese, Martina and Bartz-Beielstein, Thomas and B{\"a}ck, Thomas and Naujoks, Boris and Emmerich, Michael},
    date-added = {2019-02-23 12:24:56 +0100},
    date-modified = {2019-04-01 16:52:44 +0200},
    doi = {10.1063/1.5089970},
    eprint = {https://aip.scitation.org/doi/pdf/10.1063/1.5089970},
    journal = {AIP Conference Proceedings},
    keywords = {bartzPublic},
    number = {1},
    pages = {1-5},
    title = {Weighted ensembles in model-based global optimization},
    url = {https://aip.scitation.org/doi/abs/10.1063/1.5089970},
    volume = {2070},
    year = {2019},
    bdsk-file-1 = {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},
    bdsk-url-1 = {https://aip.scitation.org/doi/abs/10.1063/1.5089970},
    bdsk-url-2 = {https://doi.org/10.1063/1.5089970}}
  • Lorenzo Gentile, Cristian Greco, Edmondo Minisci, Thomas Bartz-Beielstein, and Massimiliano Vasile. An optimization approach for designing optimal tracking campaigns for low-resources deep-space missions. In 70th international astronautical congress, pages 1-11, Washington, United States, October 2019. IA. Online: https://strathprints.strath.ac.uk/70403/
    [Bibtex]
    @inproceedings{Gent19b,
    abstract = {This work contributes to the autonomous scheduling of orbit determination campaigns for tracking spacecraft in deep-space by developing a dedicated optimisation algorithm. Given a network of available ground stations, the developed method autonomously generates optimized tracking observation campaigns, in terms of stations to use and time of measurements, which minimize the uncertainty associated to the state of the satellite. The outcome is a set of optimal solutions characterized by different allocated budgets, among which the operators can choose the most appropriate or promising one. The developed approach relies on a Structured-Chromosome Genetic Algorithm that copes with mixed-discrete global optimization problems with variable-size design space. This operates on a hierarchical reformulation of the problem by means of revised genetic operators. The estimation of the spacecraft state and its uncertainty, given a set of measurements is performed using a sparse Gauss-Hermite Kalman Filter. The proposed approach has been tested to the design of observation campaigns for tracking a satellite in its interplanetary cruise to an asteroid. Uncertainty is considered in the initial conditions, execution errors and observation noises.},
    address = {Washington, United States},
    author = {Lorenzo Gentile and Cristian Greco and Edmondo Minisci and Thomas Bartz-Beielstein and Massimiliano Vasile},
    booktitle = {70th International Astronautical Congress},
    date-added = {2019-12-06 19:01:32 +0100},
    date-modified = {2021-07-23 19:48:42 +0200},
    keywords = {optimisation, tracking campaigns, structured-chromosome, Motor vehicles. Aeronautics. Astronautics, Aerospace Engineering, bartzPublic},
    month = {October},
    note = {Online: https://strathprints.strath.ac.uk/70403/},
    organization = {IA},
    pages = {1-11},
    title = {An optimization approach for designing optimal tracking campaigns for low-resources deep-space missions},
    url = {https://strathprints.strath.ac.uk/70403/},
    year = {2019},
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    bdsk-url-1 = {https://strathprints.strath.ac.uk/70403/}}
  • [PDF] [DOI] Lorenzo Gentile, Cristian Greco, Edmondo Minisci, Thomas Bartz-Beielstein, and Massimiliano Vasile. Structured-chromosome ga optimisation for satellite tracking. In Proceedings of the genetic and evolutionary computation conference companion, GECCO ’19, page 1955–1963, New York, NY, USA, 2019. Acm.
    [Bibtex]
    @inproceedings{Gent19a,
    acmid = {3326841},
    address = {New York, NY, USA},
    author = {Gentile, Lorenzo and Greco, Cristian and Minisci, Edmondo and Bartz-Beielstein, Thomas and Vasile, Massimiliano},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    date-added = {2019-11-29 22:12:39 +0100},
    date-modified = {2019-11-29 22:12:53 +0100},
    doi = {10.1145/3319619.3326841},
    isbn = {978-1-4503-6748-6},
    keywords = {genetic algorithms, optimisation, satellite tracking, structured-chromosome, bartzPublic},
    location = {Prague, Czech Republic},
    numpages = {9},
    pages = {1955--1963},
    publisher = {ACM},
    series = {GECCO '19},
    title = {Structured-chromosome GA Optimisation for Satellite Tracking},
    url = {http://doi.acm.org/10.1145/3319619.3326841},
    year = {2019},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/3319619.3326841},
    bdsk-url-2 = {https://doi.org/10.1145/3319619.3326841}}
  • [PDF] [DOI] Cristian Greco, Lorenzo Gentile, Gianluca Filippi, Edmondo Minisci, and Thomas Bartz-Beielstein. Autonomous generation of observation schedules for tracking satellites with structured-chromosome ga optimisation. In 2019 ieee congress on evolutionary computation (cec), pages 497-505, June 2019.
    [Bibtex]
    @inproceedings{Grec19a,
    abstract = {This paper addresses the problem of autonomous scheduling of space objects' observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces. The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.},
    author = {Greco, Cristian and Gentile, Lorenzo and Filippi, Gianluca and Minisci, Edmondo and Bartz-Beielstein, Thomas},
    booktitle = {2019 IEEE Congress on Evolutionary Computation (CEC)},
    date-added = {2019-11-29 22:08:50 +0100},
    date-modified = {2021-07-23 17:43:34 +0200},
    doi = {10.1109/CEC.2019.8790101},
    keywords = {bartzPublic, artificial satellites;genetic algorithms;object tracking;resource allocation;scheduling;search problems;state estimation;resource allocation;sequential filtering;structured-chromosome GA optimisation;structured-chromosome genetic algorithm optimiser;satellite tracking;search algorithm;mixed-discrete global optimisation problems;satellite state distribution;optimisation algorithm;state estimation routine;tracking stations;space objects;autonomous scheduling;observation schedules;Space vehicles;Extraterrestrial measurements;Optimization;Satellites;Schedules;Orbits;Mathematical model},
    month = {June},
    pages = {497-505},
    title = {Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation},
    year = {2019},
    bdsk-file-1 = {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},
    bdsk-url-1 = {https://doi.org/10.1109/CEC.2019.8790101}}
  • Cristian Greco, Lorenzo Gentile, Massimiliano Vasile, Edmondo Minisci, and Thomas Bartz-Beielstein. Robust particle filter for space objects tracking under severe uncertainty. In 2019 aas/aiaa astrodynamics specialist conference, August 2019. Online: https://strathprints.strath.ac.uk/70566/
    [Bibtex]
    @inproceedings{Grec19b,
    abstract = {This paper presents a robust particle filter approach able to handle a set-valued specification of the probability measures modelling the uncertainty structure of tracking problems. This method returns robust bounds on a quantity of interest compatibly with the infinite number of uncertain distributions specified. The importance particles are drawn and propagated only once, and the bound computation is realised by inexpensively tuning the importance weights. Furthermore, the uncertainty propagation is realised efficiently by employing an intrusive polynomial algebra technique. The developed method is finally applied to the computation of a debris-satellite collision probability in a scenario characterised by severe uncertainty.},
    author = {Cristian Greco and Lorenzo Gentile and Massimiliano Vasile and Edmondo Minisci and Thomas Bartz-Beielstein},
    booktitle = {2019 AAS/AIAA Astrodynamics Specialist Conference},
    date-added = {2019-11-29 21:54:57 +0100},
    date-modified = {2021-07-23 19:49:54 +0200},
    journal = {2019 AAS/AIAA Astrodynamics Specialist Conference},
    keywords = {uncertainty, bayesian statistics, collision avoidance, particle filter approach, Mechanical engineering and machinery, Aerospace Engineering, bartzPublic},
    month = {August},
    note = {Online: https://strathprints.strath.ac.uk/70566/},
    title = {Robust particle filter for space objects tracking under severe uncertainty},
    url = {https://strathprints.strath.ac.uk/70566/},
    year = {2019},
    bdsk-file-1 = {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},
    bdsk-url-1 = {https://strathprints.strath.ac.uk/70566/}}
  • [PDF] [DOI] Frederik Rehbach, Lorenzo Gentile, and Thomas Bartz-Beielstein. Feature selection for surrogate model-based optimization. In Proceedings of the genetic and evolutionary computation conference companion, GECCO ’19, page 399–400, New York, NY, USA, 2019. Association for computing machinery.
    [Bibtex]
    @inproceedings{Rehb18c,
    address = {New York, NY, USA},
    author = {Rehbach, Frederik and Gentile, Lorenzo and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    date-added = {2020-07-08 16:40:33 +0200},
    date-modified = {2020-07-08 16:41:35 +0200},
    doi = {10.1145/3319619.3322020},
    isbn = {9781450367486},
    keywords = {bartzPublic, dimensionality reduction, optimization, surrogates, modeling, real-world},
    location = {Prague, Czech Republic},
    numpages = {2},
    pages = {399--400},
    publisher = {Association for Computing Machinery},
    series = {GECCO '19},
    title = {Feature Selection for Surrogate Model-Based Optimization},
    url = {https://doi.org/10.1145/3319619.3322020},
    year = {2019},
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    bdsk-url-1 = {https://doi.org/10.1145/3319619.3322020}}
  • [PDF] Frederik Rehbach, Lorenzo Gentile, and Thomas Bartz-Beielstein. Variablenreduktion für surrogat-modell basierte optimierung. In Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, editors, Proc. 29th workshop computational intelligence, pages 209-216. KIT scientific publishing, 2019.
    [Bibtex]
    @inproceedings{rehb18d,
    author = {Rehbach, Frederik and Gentile, Lorenzo and Bartz-Beielstein, Thomas},
    booktitle = {Proc. 29th Workshop Computational Intelligence},
    date-added = {2019-08-08 19:26:21 +0200},
    date-modified = {2021-07-23 20:11:29 +0200},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke and Mikut, Ralf},
    keywords = {bartzPublic},
    pages = {209-216},
    publisher = {{KIT} Scientific Publishing},
    title = {Variablenreduktion f{\"u}r Surrogat-Modell basierte Optimierung},
    year = {2019},
    bdsk-file-1 = {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}}
  • [DOI] Jörg Stork, Martin Zaefferer, and Thomas Bartz-Beielstein. Improving neuroevolution efficiency by surrogate model-based optimization with phenotypic distance kernels. In Paul Kaufmann and Pedro A. Castillo, editors, Applications of evolutionary computation – 22nd international conference, evoapplications 2019, held as part of evostar 2019, leipzig, germany, april 24-26, 2019, proceedings, volume 11454 of Lecture Notes in Computer Science, page 504–519. Springer, 2019.
    [Bibtex]
    @inproceedings{Stor18c,
    author = {J{\"o}rg Stork and Martin Zaefferer and Thomas Bartz-Beielstein},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/bib/conf/evoW/StorkZB19},
    booktitle = {Applications of Evolutionary Computation - 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24-26, 2019, Proceedings},
    date-added = {2019-05-12 12:01:05 +0200},
    date-modified = {2021-07-23 17:53:11 +0200},
    doi = {10.1007/978-3-030-16692-2\_34},
    editor = {Paul Kaufmann and Pedro A. Castillo},
    keywords = {bartzPublic, nonfree},
    pages = {504--519},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Wed, 10 Apr 2019 16:43:38 +0200},
    title = {Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels},
    url = {https://doi.org/10.1007/978-3-030-16692-2\_34},
    volume = {11454},
    year = {2019},
    bdsk-url-1 = {https://doi.org/10.1007/978-3-030-16692-2%5C_34}}
  • [PDF] Jörg Stork, Martin Zaefferer, and Thomas Bartz-Beielstein. Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels. Arxiv e-prints, pages arXiv:1902.03419, Feb 2019.
    [Bibtex]
    @article{Stor18carxiv,
    adsnote = {Provided by the SAO/NASA Astrophysics Data System},
    adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190203419S},
    archiveprefix = {arXiv},
    author = {Stork, J{\"o}rg and Zaefferer, Martin and Bartz-Beielstein, Thomas},
    date-added = {2019-03-07 21:20:33 +0100},
    date-modified = {2021-07-23 17:46:56 +0200},
    eid = {arXiv:1902.03419},
    eprint = {1902.03419},
    journal = {arXiv e-prints},
    keywords = {Computer Science - Neural and Evolutionary Computing, bartzPublic},
    month = {Feb},
    pages = {arXiv:1902.03419},
    primaryclass = {cs.NE},
    title = {{Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels}},
    year = {2019},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, and A. E. Eiben. Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning. In Proceedings of the genetic and evolutionary computation conference, GECCO ’19, page 934–942, New York, NY, USA, 2019. Acm.
    [Bibtex]
    @inproceedings{Stor18d,
    acmid = {3321829},
    address = {New York, NY, USA},
    author = {Stork, J{\"o}rg and Zaefferer, Martin and Bartz-Beielstein, Thomas and Eiben, A. E.},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
    date-added = {2019-11-29 22:02:35 +0100},
    date-modified = {2019-11-29 22:02:54 +0100},
    doi = {10.1145/3321707.3321829},
    isbn = {978-1-4503-6111-8},
    keywords = {neuroevolution, reinforcement learning, surrogate models, bartzPublic},
    location = {Prague, Czech Republic},
    numpages = {9},
    pages = {934--942},
    publisher = {ACM},
    series = {GECCO '19},
    title = {Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning},
    url = {http://doi.acm.org/10.1145/3321707.3321829},
    year = {2019},
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    bdsk-url-2 = {https://doi.org/10.1145/3321707.3321829}}
  • [PDF] [DOI] Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, and A. E. Eiben. Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning. arXiv, 2019.
    [Bibtex]
    @misc{Stor18darxiv,
    abstract = {In the last years, reinforcement learning received a lot of attention. One method to solve reinforcement learning tasks is Neuroevolution, where neural networks are optimized by evolutionary algorithms. A disadvantage of Neuroevolution is that it can require numerous function evaluations, while not fully utilizing the available information from each fitness evaluation. This is especially problematic when fitness evaluations become expensive. To reduce the cost of fitness evaluations, surrogate models can be employed to partially replace the fitness function. The difficulty of surrogate modeling for Neuroevolution is the complex search space and how to compare different networks. To that end, recent studies showed that a kernel based approach, particular with phenotypic distance measures, works well. These kernels compare different networks via their behavior (phenotype) rather than their topology or encoding (genotype). In this work, we discuss the use of surrogate model-based Neuroevolution (SMB-NE) using a phenotypic distance for reinforcement learning. In detail, we investigate a) the potential of SMB-NE with respect to evaluation efficiency and b) how to select adequate input sets for the phenotypic distance measure in a reinforcement learning problem. The results indicate that we are able to considerably increase the evaluation efficiency using dynamic input sets.},
    author = {J{\"o}rg Stork and Martin Zaefferer and Thomas Bartz-Beielstein and A. E. Eiben},
    comments = {This is the authors version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Genetic and Evolutionary Computation Conference (GECCO 2019)},
    date-added = {2020-05-26 19:56:18 +0200},
    date-modified = {2021-07-24 10:28:38 +0200},
    doi = {10.1145/3321707.3321829},
    eprint = {arXiv:1907.09300},
    howpublished = {arXiv},
    keywords = {bartzPublic},
    title = {Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning},
    url = {http://arxiv.org/abs/1907.09300},
    year = {2019},
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    bdsk-url-1 = {http://arxiv.org/abs/1907.09300},
    bdsk-url-2 = {https://doi.org/10.1145/3321707.3321829}}

2018

  • [PDF] Bogdan Filipič and Thomas Bartz-Beielstein, editors. International conference on high-performance optimization in industry, HPOI 2018, number ISSN 2630-371X, Ljubljana, 10 2018. Institute Josef Stefan, Information society. ISBN 978-961-264-138-2
    [Bibtex]
    @proceedings{Fili18a,
    address = {Ljubljana},
    date-added = {2019-03-07 21:37:51 +0100},
    date-modified = {2021-07-23 20:22:21 +0200},
    editor = {Filipi{\v c}, Bogdan and Bartz-Beielstein, Thomas},
    issn = {ISSN 2630-371X},
    keywords = {bartzPublic},
    month = {10},
    note = {ISBN 978-961-264-138-2},
    number = {ISSN 2630-371X},
    organization = {Institute Josef Stefan},
    publisher = {Information Society},
    title = {International Conference on High-Performance Optimization in Industry, {HPOI} 2018},
    year = {2018},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korosec, Nouredine Melab, Boris Naujoks, and El-Ghazali Talbi. Potential complex optimisation problems in science and industry. In Synergy – synergy for smart multi-objective optimization, pages 1-51. Synergy project, January 2018.
    [Bibtex]
    @incollection{Bart18p,
    author = {Bartz-Beielstein, Thomas and Filipi{\v c}, Bogdan and Korosec, Peter and Melab, Nouredine and Naujoks, Boris and Talbi, El-Ghazali},
    booktitle = {Synergy - Synergy for Smart Multi-Objective Optimization},
    date-added = {2019-03-07 21:26:13 +0100},
    date-modified = {2019-03-07 21:33:07 +0100},
    keywords = {bartzPublic},
    month = {January},
    pages = {1-51},
    publisher = {Synergy Project},
    title = {Potential complex optimisation problems in science and industry},
    year = {2018},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Martin Zaefferer, and Quoc Cuong Pham. Optimization via multimodel simulation (preprint). CIplus 1/2018, Technische Hochschule Köln, 2018. Preprint: https://doi.org/10.1007/s00158-018-1934-2
    [Bibtex]
    @techreport{Bart16e2cos,
    abstract = {Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. Combining results from models with different input-output structures might improve and accelerate the optimization process. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.},
    author = {Thomas Bartz-Beielstein and Martin Zaefferer and Quoc Cuong Pham},
    date-added = {2019-01-31 23:34:31 +0100},
    date-modified = {2021-07-23 20:26:32 +0200},
    institution = {Technische Hochschule K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    note = {Preprint: https://doi.org/10.1007/s00158-018-1934-2},
    number = {1/2018},
    pages = {16},
    series = {CIplus},
    title = {Optimization via Multimodel Simulation (Preprint)},
    type = {CIplus},
    year = {2018},
    bdsk-file-1 = {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}}
  • [DOI] Thomas Bartz-Beielstein, Martin Zaefferer, and Quoc Cuong Pham. Optimization via multimodel simulation. Structural and multidisciplinary optimization, 58(3):919–933, Feb 2018.
    [Bibtex]
    @article{Bart16e,
    abstract = {Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. We believe that OMMS improves the robustness of the optimization, accelerates the optimization-via-simulation process, and provides a unified approach. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gasses. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.},
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin and Pham, Quoc Cuong},
    date-added = {2018-05-05 19:34:36 +0000},
    date-modified = {2019-03-07 20:52:26 +0100},
    day = {12},
    doi = {10.1007/s00158-018-1934-2},
    issn = {1615-1488},
    journal = {Structural and Multidisciplinary Optimization},
    keywords = {bartzPublic, nonfree},
    month = {Feb},
    number = {3},
    pages = {919--933},
    title = {Optimization via multimodel simulation},
    url = {https://doi.org/10.1007/s00158-018-1934-2},
    volume = {58},
    year = {2018},
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    bdsk-url-1 = {https://doi.org/10.1007/s00158-018-1934-2}}
  • Beate Breiderhoff, Boris Naujoks, Thomas Bartz-Beielstein, and Bogdan Filipič. Expensive optimisation exemplified by ecg simulator parameter tuning. In Bogdan Filipič and Thomas Bartz-Beielstein, editors, International conference on high-performance optimization in industry, HPOI 2018, pages 15-19, Ljubljana, 10 2018.
    [Bibtex]
    @inproceedings{Brei18a,
    address = {Ljubljana},
    author = {Breiderhoff, Beate and Naujoks, Boris and Bartz-Beielstein, Thomas and Filipi{\v c}, Bogdan},
    booktitle = {International Conference on High-Performance Optimization in Industry, {HPOI} 2018},
    date-added = {2019-03-07 21:46:42 +0100},
    date-modified = {2019-03-07 21:48:58 +0100},
    editor = {Filipi{\v c}, Bogdan and Bartz-Beielstein, Thomas},
    keywords = {bartzPublic},
    month = {10},
    pages = {15-19},
    title = {Expensive Optimisation Exemplified by ECG Simulator Parameter Tuning},
    year = {2018}}
  • [PDF] Margarita Alejandra Rebolledo Coy and Thomas Bartz-Beielstein. Modelling zero-inflated rainfall data through the use of gaussian process and bayesian regression. CIplus Report 5, Technische Hochschule Köln, 2018.
    [Bibtex]
    @techreport{Rebo17ccos,
    abstract = {Rainfall is a key parameter for understanding the water cycle. An accurate rainfall measurement is vital in the development of hydrological models. By means of indirect measurement, satellites can nowadays estimate the rainfall around the world. However, these measurements are not always accurate. As a first approach to generate a bias-corrected rainfall estimate using satellite data, the performance of Gaussian process and Bayesian regression is studied. The results show Gaussian process as the better option for this dataset but leave place to improvements on both modelling strategies.},
    author = {Margarita Alejandra Rebolledo Coy and Thomas Bartz-Beielstein},
    date-added = {2018-12-02 18:30:58 +0100},
    date-modified = {2019-03-07 20:56:12 +0100},
    institution = {Technische Hochschule K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    number = {5},
    pages = {3},
    title = {Modelling Zero-inflated Rainfall Data through the Use of Gaussian Process and Bayesian Regression},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-7832},
    year = {2018},
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    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-7832}}
  • [DOI] Lorenzo Gentile, Martin Zaefferer, Dario Giugliano, Haofeng Chen, and Thomas Bartz-Beielstein. Surrogate assisted optimization of particle reinforced metal matrix composites. In Proceedings of the genetic and evolutionary computation conference, GECCO ’18, page 1238–1245, New York, NY, USA, 2018. Acm.
    [Bibtex]
    @inproceedings{Gent18b,
    acmid = {3205574},
    address = {New York, NY, USA},
    author = {Gentile, Lorenzo and Zaefferer, Martin and Giugliano, Dario and Chen, Haofeng and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
    date-added = {2018-11-16 21:49:09 +0100},
    date-modified = {2018-11-16 21:51:27 +0100},
    doi = {10.1145/3205455.3205574},
    isbn = {978-1-4503-5618-3},
    keywords = {finite element methods, multilevel optimization, optimization under uncertainty, parameter optimization, surrogate model based optimization, bartzPublic, nonfree},
    location = {Kyoto, Japan},
    numpages = {8},
    pages = {1238--1245},
    publisher = {ACM},
    series = {GECCO '18},
    title = {Surrogate Assisted Optimization of Particle Reinforced Metal Matrix Composites},
    url = {http://doi.acm.org/10.1145/3205455.3205574},
    year = {2018},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/3205455.3205574},
    bdsk-url-2 = {https://doi.org/10.1145/3205455.3205574}}
  • [PDF] [DOI] Frederik Rehbach, Martin Zaefferer, Jörg Stork, and Thomas Bartz-Beielstein. Comparison of parallel surrogate-assisted optimization approaches. In Proceedings of the genetic and evolutionary computation conference, GECCO ’18, page 1348–1355, New York, NY, USA, 2018. Association for computing machinery.
    [Bibtex]
    @inproceedings{Rehb17a,
    address = {New York, NY, USA},
    author = {Rehbach, Frederik and Zaefferer, Martin and Stork, J{\"o}rg and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
    date-added = {2020-07-08 16:42:00 +0200},
    date-modified = {2020-07-08 16:42:36 +0200},
    doi = {10.1145/3205455.3205587},
    isbn = {9781450356183},
    keywords = {modeling, parallelization, optimization, surrogates, electrostatic precipitator, bartzPublic, nonfree},
    location = {Kyoto, Japan},
    numpages = {8},
    pages = {1348--1355},
    publisher = {Association for Computing Machinery},
    series = {GECCO '18},
    title = {Comparison of Parallel Surrogate-Assisted Optimization Approaches},
    url = {https://doi.org/10.1145/3205455.3205587},
    year = {2018},
    bdsk-url-1 = {https://doi.org/10.1145/3205455.3205587}}
  • [PDF] Frederik Rehbach, Martin Zaefferer, Jörg Stork, and Thomas Bartz-Beielstein. Comparison of parallel surrogate-assisted optimization approaches. CIplus Report 7/2018, TH Köln, 2018.
    [Bibtex]
    @techreport{Rehb17acos,
    abstract = {The availability of several CPU cores on current computers enables parallelization and increases the computational power significantly. Optimization algorithms have to be adapted to exploit these highly parallelized systems and evaluate multiple candidate solutions in each iteration. This issue is especially challenging for expensive optimization problems, where surrogate models are employed to reduce the load of objective function evaluations. This paper compares different approaches for surrogate modelbased optimization in parallel environments. Additionally, an easy to use method, which was developed for an industrial project, is proposed. All described algorithms are tested with a variety of standard benchmark functions. Furthermore, they are applied to a real-world engineering problem, the electrostatic precipitator problem. Expensive computational fluid dynamics simulations are required to estimate the performance of the precipitator. The task is to optimize a gas-distribution system so that a desired velocity distribution is achieved for the gas flow throughout the precipitator. The vast amount of possible configurations leads to a complex discrete valued optimization problem. The experiments indicate that a hybrid approach works best, which proposes candidate solutions based on different surrogate model-based infill criteria and evolutionary operators.},
    author = {Frederik Rehbach and Martin Zaefferer and J{\"o}rg Stork and Thomas Bartz-Beielstein},
    date-added = {2018-12-02 18:28:32 +0100},
    date-modified = {2021-07-23 20:57:58 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    number = {7/2018},
    pages = {12},
    title = {Comparison of Parallel Surrogate-Assisted Optimization Approaches},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-7899},
    year = {2018},
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    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-7899}}
  • [PDF] Frederik Rehbach and Thomas Bartz-Beielstein. Bridging theory and practice through modular graphical user interfaces. In Bogdan Filipič and Thomas Bartz-Beielstein, editors, International conference on high-performance optimization in industry, HPOI 2018, pages 11-15, Ljubljana, 10 2018.
    [Bibtex]
    @inproceedings{Rehb18a,
    address = {Ljubljana},
    author = {Frederik Rehbach and Thomas Bartz-Beielstein},
    booktitle = {International Conference on High-Performance Optimization in Industry, {HPOI} 2018},
    date-added = {2018-09-14 20:19:51 +0200},
    date-modified = {2019-05-27 21:36:51 +0200},
    editor = {Filipi{\v c}, Bogdan and Bartz-Beielstein, Thomas},
    keywords = {bartzPublic, KIOpt},
    month = {10},
    pages = {11-15},
    title = {Bridging Theory and Practice Through Modular Graphical User Interfaces},
    year = {2018},
    bdsk-file-1 = {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}}
  • Andre Schagen, Frederik Rehbach, and Thomas Bartz-Beielstein. Model-based evolutionary algorithm for optimization of gas distribution systems in power plant electrostatic precipitators. International journal for generation and storage of electricity and heat, 9:65-72, 2018.
    [Bibtex]
    @article{Scha18a,
    author = {Schagen, Andre and Rehbach, Frederik and Bartz-Beielstein, Thomas},
    date-added = {2018-10-28 10:32:49 +0100},
    date-modified = {2019-05-10 23:25:24 +0200},
    journal = {International Journal for Generation and Storage of Electricity and Heat},
    keywords = {bartzPublic, nonfree},
    pages = {65-72},
    title = {Model-based evolutionary algorithm for optimization of gas distribution systems in power plant electrostatic precipitators},
    volume = {9},
    year = {2018},
    bdsk-file-1 = {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}}
  • [PDF] Jörg Stork, Martin Zaefferer, and Thomas Bartz-Beielstein. Distance-based kernels for surrogate model-based neuroevolution. arXiv, 2018.
    [Bibtex]
    @misc{Stor18aarxiv,
    abstract = {The topology optimization of artificial neural networks can be particularly difficult if the fitness evaluations require expensive experiments or simulations. For that reason, the optimization methods may need to be supported by surrogate models. We propose different distances for a suitable surrogate model, and compare them in a simple numerical test scenario.},
    author = {J{\"o}rg Stork and Martin Zaefferer and Thomas Bartz-Beielstein},
    comments = {4 pages, 1 figure. This publication was accepted to the Developmental Neural Networks Workshop of the Parallel Problem Solving from Nature 2018 (PPSN XV) conference},
    date-added = {2019-01-31 23:43:22 +0100},
    date-modified = {2021-07-23 20:59:49 +0200},
    eprint = {arXiv:1807.07839},
    howpublished = {arXiv},
    keywords = {bartzPublic},
    title = {Distance-based Kernels for Surrogate Model-based Neuroevolution},
    url = {http://arxiv.org/abs/1807.07839},
    year = {2018},
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    bdsk-url-1 = {http://arxiv.org/abs/1807.07839}}
  • [PDF] Jörg Stork and Thomas Bartz-Beielstein. Global optimization strategies: analogies to human behavior. CIplus Report 2/2018, TH Köln, 2018.
    [Bibtex]
    @techreport{Stor17dcos,
    abstract = {This short article presents a new taxonomy for modern global optimization heuristics based on analogies to human behavior.},
    author = {J{\"o}rg Stork and Thomas Bartz-Beielstein},
    date-added = {2018-12-02 18:36:33 +0100},
    date-modified = {2021-07-23 21:00:35 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    number = {2/2018},
    pages = {3},
    title = {Global Optimization Strategies: Analogies to Human Behavior},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-6967},
    year = {2018},
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    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-6967}}
  • [PDF] Jörg Stork, A. E. Eiben, and Thomas Bartz-Beielstein. A new taxonomy of continuous global optimization algorithms. CIplus Report 4/2018, TH Köln, 2018.
    [Bibtex]
    @techreport{Stor16acos,
    abstract = {Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field. This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.},
    author = {J{\"o}rg Stork and A.E. Eiben and Thomas Bartz-Beielstein},
    date-added = {2018-12-02 18:33:16 +0100},
    date-modified = {2021-07-23 21:15:34 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    number = {4/2018},
    pages = {52},
    series = {CIplus},
    title = {A new Taxonomy of Continuous Global Optimization Algorithms},
    type = {CIplus Report},
    url = {https://cos.bibl.th-koeln.de/frontdoor/index/index/searchtype/series/id/8/docId/753/start/3/rows/10},
    year = {2018},
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    bdsk-url-1 = {https://cos.bibl.th-koeln.de/frontdoor/index/index/searchtype/series/id/8/docId/753/start/3/rows/10}}
  • [PDF] Jörg Stork, A. E. Eiben, and Thomas Bartz-Beielstein. A new taxonomy of continuous global optimization algorithms. arXiv, 2018.
    [Bibtex]
    @misc{Stor16aarxiv,
    abstract = {Surrogate-based optimization and nature-inspired metaheuristics have become the state-of-the-art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field. This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.},
    author = {J{\"o}rg Stork and A. E. Eiben and Thomas Bartz-Beielstein},
    comments = {47 pages total, 36 written pages, 3 full-page figures},
    date-added = {2019-01-31 23:40:13 +0100},
    date-modified = {2021-07-23 21:03:33 +0200},
    eprint = {arXiv:1808.08818},
    howpublished = {arXiv},
    keywords = {bartzPublic},
    title = {A new Taxonomy of Continuous Global Optimization Algorithms},
    url = {http://arxiv.org/abs/1808.08818},
    year = {2018},
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    bdsk-url-1 = {http://arxiv.org/abs/1808.08818}}
  • [PDF] Aljosa Vodopija, Jörg Stork, Thomas Bartz-Beielstein, and Bogdan Filipič. Model-based multiobjective optimization of elevator group control. In Bogdan Filipič and Thomas Bartz-Beielstein, editors, International conference on high-performance optimization in industry, HPOI 2018, pages 43-46, Ljubljana, 10 2018.
    [Bibtex]
    @inproceedings{Vodo18a,
    address = {Ljubljana},
    author = {Vodopija, Aljosa and Stork, J{\"o}rg and Bartz-Beielstein, Thomas and Filipi{\v c}, Bogdan},
    booktitle = {International Conference on High-Performance Optimization in Industry, {HPOI} 2018},
    date-added = {2019-03-07 21:49:47 +0100},
    date-modified = {2019-03-07 21:51:23 +0100},
    editor = {Filipi{\v c}, Bogdan and Bartz-Beielstein, Thomas},
    keywords = {bartzPublic},
    month = {10},
    pages = {43-46},
    title = {Model-Based Multiobjective Optimization of Elevator Group Control},
    year = {2018},
    bdsk-file-1 = {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}}
  • [DOI] Martin Zaefferer, Jörg Stork, Oliver Flasch, and Thomas Bartz-Beielstein. Linear combination of distance measures for surrogate models in genetic programming. In Anne Auger, Carlos M. Fonseca, Nuno Lourenço, Penousal Machado, Luís Paquete, and Darrell Whitley, editors, Parallel problem solving from nature – PPSN XV: 15th international conference, volume 11102 of Lecture Notes in Computer Science, page 220–231, Coimbra, Portugal, 2018. Springer.
    [Bibtex]
    @inproceedings{Zaef18b,
    address = {Coimbra, Portugal},
    author = {Martin Zaefferer and J{\"o}rg Stork and Oliver Flasch and Thomas Bartz-Beielstein},
    booktitle = {Parallel Problem Solving from Nature {\textendash} {PPSN} {XV}: 15th International Conference},
    date-added = {2018-09-14 20:23:55 +0200},
    date-modified = {2019-01-31 23:38:57 +0100},
    doi = {10.1007/978-3-319-99259-4_18},
    editor = {Anne Auger and Carlos M. Fonseca and Nuno Louren\c{c}o and Penousal Machado and Lu\'{i}s Paquete and Darrell Whitley},
    keywords = {bartzPublic, nonfree},
    month = sep,
    pages = {220--231},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {Linear Combination of Distance Measures for Surrogate Models in Genetic Programming},
    volume = {11102},
    year = {2018},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-319-99259-4_18}}
  • [PDF] Martin Zaefferer, Jörg Stork, Oliver Flasch, and Thomas Bartz-Beielstein. Linear combination of distance measures for surrogate models in genetic programming. arXiv, 2018.
    [Bibtex]
    @misc{Zaef18barxiv,
    archiveprefix = {arXiv},
    author = {Martin Zaefferer and J{\"o}rg Stork and Oliver Flasch and Thomas Bartz-Beielstein},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/bib/journals/corr/abs-1807-01019},
    date-added = {2019-05-12 12:06:11 +0200},
    date-modified = {2021-07-23 21:04:49 +0200},
    eprint = {1807.01019},
    howpublished = {arXiv},
    keywords = {bartzPublic, free},
    timestamp = {Mon, 13 Aug 2018 16:46:09 +0200},
    title = {Linear Combination of Distance Measures for Surrogate Models in Genetic Programming},
    url = {http://arxiv.org/abs/1807.01019},
    volume = {abs/1807.01019},
    year = {2018},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://arxiv.org/abs/1807.01019}}
  • [PDF] Martin Zaefferer, Thomas Bartz-Beielstein, and Günter Rudolph. An empirical approach for probing the definiteness of kernels. arXiv, 2018.
    [Bibtex]
    @misc{Zaef14earxiv,
    archiveprefix = {arXiv},
    author = {Martin Zaefferer and Thomas Bartz-Beielstein and G{\"u}nter Rudolph},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/bib/journals/corr/abs-1807-03555},
    date-added = {2019-05-12 12:08:24 +0200},
    date-modified = {2021-07-23 21:16:54 +0200},
    eprint = {1807.03555},
    howpublished = {arXiv},
    keywords = {bartzPublic, free},
    timestamp = {Mon, 13 Aug 2018 16:48:38 +0200},
    title = {An Empirical Approach For Probing the Definiteness of Kernels},
    url = {http://arxiv.org/abs/1807.03555},
    volume = {abs/1807.03555},
    year = {2018},
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    bdsk-url-1 = {http://arxiv.org/abs/1807.03555}}

2017

  • [DOI] Thomas Bartz-Beielstein and Martin Zaefferer. Model-based methods for continuous and discrete global optimization. Applied soft computing, 55:154-167, 2017.
    [Bibtex]
    @article{Bart16n,
    abstract = {Abstract The use of surrogate models is a standard method for dealing with complex real-world optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article takes this development into consideration. The first part presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part examines discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in continuous and discrete application domains. },
    author = {Thomas Bartz-Beielstein and Martin Zaefferer},
    date-added = {2017-02-22 10:05:10 +0000},
    date-modified = {2017-11-22 09:19:31 +0000},
    doi = {10.1016/j.asoc.2017.01.039},
    issn = {1568-4946},
    journal = {Applied Soft Computing},
    keywords = {Evolutionary computation, owos, frie17a, bartzPublic, nonfree},
    pages = {154 - 167},
    title = {Model-based methods for continuous and discrete global optimization},
    url = {http://www.sciencedirect.com/science/article/pii/S1568494617300546},
    volume = {55},
    year = {2017},
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    bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S1568494617300546},
    bdsk-url-2 = {http://dx.doi.org/10.1016/j.asoc.2017.01.039}}
  • [PDF] Thomas Bartz-Beielstein, Steffen Moritz, Jan Strohschein, Thorsten Winterberg, Dimitri Gross, and Ralf Seger. Trinkwassersicherheit mit Predictive Analytics und Oracle. DOAG news, (1):18-23, 2017.
    [Bibtex]
    @article{Wint17a,
    author = {Thomas Bartz-Beielstein and Steffen Moritz and Jan Strohschein and Thorsten Winterberg and Dimitri Gross and Ralf Seger},
    date-added = {2017-02-01 17:43:17 +0000},
    date-modified = {2017-08-17 10:47:06 +0000},
    journal = {{DOAG} news},
    keywords = {bartzPublic},
    number = {1},
    pages = {18-23},
    title = {{Trinkwassersicherheit mit Predictive Analytics und Oracle}},
    url = {http://bs.doag.org},
    year = {2017},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://bs.doag.org}}
  • [PDF] Thomas Bartz-Beielstein, Martin Zaefferer, Jörg Stork, and Sebastian Krey. The revised sequential parameter optimization toolbox. In Tobias Verbeke, editor, The R User Conference, useR! 2017, page 151, July 2017.
    [Bibtex]
    @inproceedings{Bart17i,
    abstract = {Real-world optimization problems often have very high complexity, due to multi-modality, constraints, noise or other crucial problem features. For solving these optimization problems a large collection of methods are available. Most of these methods require to set a number of parameters, which have a significant impact on the optimization performance. Hence, a lot of experience and knowledge about the problem is necessary to give the best possible results. This situation grows worse if the optimization algorithm faces the additional difficulty of strong restrictions on resources, especially time, money or number of experiments.
    Sequential parameter optimization (Bartz-Beielstein, Lasarczyk, and Preuss 2005) is a heuristic combining classical and modern statistical techniques for the purpose of efficient optimization. It can be applied in two manners:
    - to efficiently tune and select the parameters of other search algorithms, or
    - to optimize expensive-to-evaluate problems directly, via shifting the load of evaluations to a surrogate model.
    SPO is especially useful in scenarios where
    - no experience of how to choose the parameter setting of an algorithm is available,
    - a comparison with other algorithms is needed,
    - an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem, and
    - the objective function is a black-box and expensive to evaluate.
    The Sequential Parameter Optimization Toolbox SPOT provides enhanced statistical techniques such as design and analysis of computer experiments, different methods for surrogate modeling and optimization to effectively use sequential parameter optimization in the above mentioned scenarios.
    Version 2 of the SPOT package is a complete redesign and rewrite of the original R package. Most function interfaces were redesigned to give a more streamlined usage experience. At the same time, modular and transparent code structures allow for increased extensibility. In addition, some new developments were added to the SPOT package. A Kriging model implementation, based on earlier Matlab code by Forrester et al. (Forrester, Sobester, and Keane 2008), has been extended to allow for the usage of categorical inputs. Additionally, it is now possible to use stacking for the construction of ensemble learners (Bartz-Beielstein and Zaefferer 2017). This allows for the creation of models with a far higher predictive performance, by combining the strengths of different modeling approaches.
    In this presentation we show how the new interface of SPOT can be used to efficiently optimize the geometry of an industrial dust filter (cyclone). Based on a simplified simulation of this real world industry problem, some of the core features of SPOT are demonstrated.
    References Bartz-Beielstein, Thomas, and Martin Zaefferer. 2017. ``Model-Based Methods for Continuous and Discrete Global Optimization.'' Applied Soft Computing 55: 154--67. doi:10.1016/j.asoc.2017.01.039.
    Bartz-Beielstein, Thomas, Christian Lasarczyk, and Mike Preuss. 2005. ``Sequential Parameter Optimization.'' In Proceedings Congress on Evolutionary Computation 2005 (Cec'05), 1553. Edinburgh, Scotland. https://www.spotseven.de/wp-content/papercite-data/pdf/blp05.pdf.
    Forrester, Alexander, Andras Sobester, and Andy Keane. 2008. Engineering Design via Surrogate Modelling. Wiley.},
    author = {Thomas Bartz-Beielstein and Martin Zaefferer and J{\"o}rg Stork and Sebastian Krey},
    booktitle = {{The R User Conference, useR! 2017}},
    date-added = {2017-03-30 14:27:48 +0000},
    date-modified = {2017-12-12 13:19:00 +0000},
    editor = {Tobias Verbeke},
    keywords = {bartzPublic},
    month = {July},
    pages = {151},
    title = {The Revised Sequential Parameter Optimization Toolbox},
    year = {2017},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Jochen Blaurock, Sebastian Krey, Yixi Fu, Niclas Kallenbach, and Marc Möller. Structural Health Monitoring von Faserverbundstrukturen mittels Piezosensoren – Untersuchungen zum experimentellen Design. CIplus 2/2017, TH Köln, 2017.
    [Bibtex]
    @techreport{Bart17hcos,
    author = {Thomas Bartz-Beielstein and Jochen Blaurock and Sebastian Krey and Yixi Fu and Niclas Kallenbach and Marc M{\"o}ller},
    date-added = {2017-04-25 09:36:48 +0000},
    date-modified = {2021-07-23 21:23:11 +0200},
    doi = {10.13140/RG.2.2.27207.09126},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    number = {2/2017},
    pages = {22},
    series = {CIplus},
    title = {{Structural Health Monitoring von Faserverbundstrukturen mittels Piezosensoren - Untersuchungen zum experimentellen Design}},
    type = {CIplus},
    url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4727},
    year = {2017},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4727},
    bdsk-url-2 = {http://dx.doi.org/10.13140/RG.2.2.27207.09126}}
  • [PDF] Thomas Bartz-Beielstein, Lorenzo Gentile, and Martin Zaefferer. In a nutshell: sequential parameter optimization. arXiv, 2017. https://arxiv.org/abs/1712.04076v1
    [Bibtex]
    @misc{Bart17parxiv,
    abstract = {The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the sequential parameter optimization toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underling concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm's behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.},
    author = {Thomas Bartz-Beielstein and Lorenzo Gentile and Martin Zaefferer},
    date = {2017-12-12},
    date-added = {2018-01-07 22:20:21 +0000},
    date-modified = {2021-07-23 21:21:05 +0200},
    eprint = {1712.04076v1},
    eprintclass = {cs.MS},
    eprinttype = {arXiv},
    howpublished = {arXiv},
    keywords = {cs.MS, cs.AI, math.OC, ispublished, bartzPublic},
    note = {https://arxiv.org/abs/1712.04076v1},
    title = {In a Nutshell: Sequential Parameter Optimization},
    year = {2017},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Lorenzo Gentile, and Martin Zaefferer. In a nutshell: sequential parameter optimization. CIplus Report 7/2017, TH Köln, Dec 2017. Cologne Open Science
    [Bibtex]
    @techreport{Bart17pcos,
    author = {Bartz-Beielstein, Thomas and Gentile, Lorenzo and Zaefferer, Martin},
    date-added = {2017-11-02 18:28:59 +0000},
    date-modified = {2021-07-23 21:18:25 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    month = {Dec},
    note = {Cologne Open Science},
    number = {7/2017},
    title = {In a Nutshell: Sequential Parameter Optimization},
    type = {CIplus Report},
    year = {2017},
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  • Thomas Bartz-Beielstein. SPOTSeven Lab: Jahresbericht 2014/15. Technical Report, TH Köln, 2017.
    [Bibtex]
    @techreport{Bart17r,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2018-01-07 15:45:07 +0000},
    date-modified = {2021-07-24 10:27:55 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    title = {{SPOTSeven Lab: Jahresbericht 2014/15}},
    year = {2017}}
  • [DOI] Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, A. E. Eiben, Thomas Bartz-Beielstein, and Evert Haasdijk. Can social learning increase learning speed, performance or both?. In Proceedings of ecal 2017 the 14th european conference on artificial life, volume 14 of ALIFE 2021: The 2021 Conference on Artificial Life, pages 200-207, 09 2017.
    [Bibtex]
    @inproceedings{Hein17b,
    author = {Jacqueline Heinerman and J{\"o}rg Stork and Margarita Alejandra Rebolledo Coy and Julien Hubert and A.E. Eiben and Thomas Bartz-Beielstein and Evert Haasdijk},
    booktitle = {Proceedings of ECAL 2017 the 14th European Conference on Artificial Life},
    date-added = {2017-06-06 18:35:44 +0000},
    date-modified = {2021-07-24 10:26:32 +0200},
    doi = {10.1162/isal_a_036},
    keywords = {bartzPublic, nonfree},
    month = {09},
    pages = {200-207},
    series = {ALIFE 2021: The 2021 Conference on Artificial Life},
    title = {Can Social Learning Increase Learning Speed, Performance or Both?},
    volume = {14},
    year = {2017},
    bdsk-url-1 = {https://doi.org/10.1162/isal_a_036}}
  • [PDF] [DOI] Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, A. E. Eiben, Thomas Bartz-Beielstein, and Evert Haasdijk. Is social learning more than parameter tuning?. In Proceedings of the genetic and evolutionary computation conference companion, GECCO ’17, page 63–64, New York, NY, USA, 2017. Association for computing machinery.
    [Bibtex]
    @inproceedings{Hein17a,
    abstract = {Social learning enables multiple robots to share learned experiences while completing
    a task. The literature offers examples where robots trained with social learning reach
    a higher performance compared to their individual learning counterparts [e.g, 2, 4].
    No explanation has been advanced for that observation. In this research, we present
    experimental results suggesting that a lack of tuning of the parameters in social
    learning experiments could be the cause. In other words: the better the parameter
    settings are tuned, the less social learning can improve the system performance.},
    address = {New York, NY, USA},
    author = {Heinerman, Jacqueline and Stork, J\"{o}rg and Coy, Margarita Alejandra Rebolledo and Hubert, Julien and Eiben, A. E. and Bartz-Beielstein, Thomas and Haasdijk, Evert},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    date-added = {2021-07-23 21:25:58 +0200},
    date-modified = {2021-07-23 21:28:18 +0200},
    doi = {10.1145/3067695.3076059},
    isbn = {9781450349390},
    keywords = {social learning, evolutionary robotics, parameter tuning, neural networks, bartzPublic},
    location = {Berlin, Germany},
    numpages = {2},
    pages = {63--64},
    publisher = {Association for Computing Machinery},
    series = {GECCO '17},
    title = {Is Social Learning More than Parameter Tuning?},
    url = {https://doi.org/10.1145/3067695.3076059},
    year = {2017},
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    bdsk-url-1 = {https://doi.org/10.1145/3067695.3076059}}
  • [PDF] [DOI] Christian Jung, Martin Zaefferer, Thomas Bartz-Beielstein, and Günter Rudolph. Metamodel-based optimization of hot rolling processes in the metal industry. The international journal of advanced manufacturing technology, 90(1):421–435, Apr 2017.
    [Bibtex]
    @article{Jung14b,
    abstract = {To maximize the throughput of a hot rolling mill, the number of passes has to be reduced. This can be achieved by maximizing the thickness reduction in each pass. For this purpose, exact predictions of roll force and torque are required. Hence, the predictive models that describe the physical behavior of the product have to be accurate and cover a wide range of different materials. Due to market requirements, a lot of new materials are tested and rolled. If these materials are chosen to be rolled more often, a suitable flow curve has to be established. It is not reasonable to determine those flow curves in laboratory, because of costs and time. A strong demand for quick parameter determination and the optimization of flow curve parameter with minimum costs is the logical consequence. Therefore, parameter estimation and the optimization with real data, which were collected during previous runs, is a promising idea. Producers benefit from this data-driven approach and receive a huge gain in flexibility when rolling new materials, optimizing current production, and increasing quality. This concept would also allow to optimize flow curve parameters, which have already been treated by standard methods. In this article, a new data-driven approach for predicting the physical behavior of the product and setting important parameters is presented. We demonstrate how the prediction quality of the roll force and roll torque can be optimized sustainably. This offers the opportunity to continuously increase the workload in each pass to the theoretical maximum while product quality and process stability can also be improved.},
    author = {Jung, Christian and Zaefferer, Martin and Bartz-Beielstein, Thomas and Rudolph, G{\"u}nter},
    date-added = {2016-11-11 05:37:11 +0000},
    date-modified = {2019-11-23 09:48:30 +0100},
    doi = {10.1007/s00170-016-9386-6},
    issn = {1433-3015},
    journal = {The International Journal of Advanced Manufacturing Technology},
    keywords = {bartzPublic, Bart16n, nonfree},
    month = {Apr},
    number = {1},
    pages = {421--435},
    title = {Metamodel-based optimization of hot rolling processes in the metal industry},
    url = {https://doi.org/10.1007/s00170-016-9386-6},
    volume = {90},
    year = {2017},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/s00170-016-9386-6},
    bdsk-url-2 = {https://doi.org/10.1007/s00170-016-9386-6}}
  • [PDF] Christian Jung, Martin Zaefferer, Thomas Bartz-Beielstein, and Günter Rudolph. Meta-model based optimization of hot rolling processes in the metal industry. CIplus Report 6/2017, TH Köln, 2017.
    [Bibtex]
    @techreport{Jung14bcos,
    abstract = {To maximize the throughput of a hot rolling mill, the number of passes has to be reduced. This can be achieved by maximizing the thickness reduction in each pass. For this purpose, exact predictions of roll force and torque are required. Hence, the predictive models that describe the physical behavior of the product have to be accurate and cover a wide range of different materials. Due to market requirements a lot of new materials are tested and rolled. If these materials are chosen to be rolled more often, a suitable flow curve has to be established. It is not reasonable to determine those flow curves in laboratory, because of costs and time. A strong demand for quick parameter determination and the optimization of flow curve parameter with minimum costs is the logical consequence. Therefore parameter estimation and the optimization with real data, which were collected during previous runs, is a promising idea. Producers benefit from this data-driven approach and receive a huge gain in flexibility when rolling new materials, optimizing current production, and increasing quality. This concept would also allow to optimize flow curve parameters, which have already been treated by standard methods. In this article, a new data-driven approach for predicting the physical behavior of the product and setting important parameters is presented. We demonstrate how the prediction quality of the roll force and roll torque can be optimized sustainably. This offers the opportunity to continuously increase the workload in each pass to the theoretical maximum while product quality and process stability can also be improved.},
    author = {Christian Jung and Martin Zaefferer and Thomas Bartz-Beielstein and G{\"u}nter Rudolph},
    date-added = {2019-12-09 11:13:54 +0100},
    date-modified = {2021-07-23 21:30:54 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    language = {en},
    number = {6/2017},
    pages = {18},
    title = {Meta-model based optimization of hot rolling processes in the metal industry},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-5585},
    year = {2017},
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    bdsk-url-1 = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-5585}}
  • [PDF] Sebastian Krey, Thomas Bartz-Beielstein, Yixi Fu, and Carolin Gorec. Structural health monitoring for resource-efficient usage of fibre-reinforced plastic. In Hans Kestler, Ludwig Lausser, Johann Kraus, Matthias Schmid, and Axel Fürstberger, editors, Statistical computing 2017: abstracts der 49. arbeitstagung der arbeitsgruppen statistical computing (gmds/ibs-dr), klassifikation und datenanalyse in den biowissenschaften (gfkl), 23.07. – 25.07.2017, schloss reisensburg (günzburg), volume 2017 of Ulmer Informatik-Berichte, page 9, 2017. Open Access Repositorium der Universität Ulm
    [Bibtex]
    @inproceedings{Krey17a,
    author = {Sebastian Krey and Thomas Bartz-Beielstein and Yixi Fu and Carolin Gorec},
    booktitle = {Statistical Computing 2017: Abstracts der 49. Arbeitstagung der Arbeitsgruppen Statistical Computing (GMDS/IBS-DR), Klassifikation und Datenanalyse in den Biowissenschaften (GfKl), 23.07. - 25.07.2017, Schloss Reisensburg (G{\"u}nzburg)},
    date-added = {2018-01-07 21:29:13 +0000},
    date-modified = {2021-07-23 21:36:08 +0200},
    editor = {Hans Kestler and Ludwig Lausser and Johann Kraus and Matthias Schmid and Axel F{\"u}rstberger},
    keywords = {bartzPublic, public},
    note = {Open Access Repositorium der Universit{\"a}t Ulm},
    number = {01},
    pages = {9},
    series = {Ulmer Informatik-Berichte},
    title = {Structural Health Monitoring for Resource-efficient Usage of Fibre-Reinforced Plastic},
    volume = {2017},
    year = {2017},
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  • [PDF] Steffen Moritz, Thomas Bartz-Beielstein, Jan Strohschein, Ralf Seger, and Dimitri Gross. Trinkwasser-sicherheit mit predictive analytics und oracle. CIplus Report 4/2017, TH Köln, 2017.
    [Bibtex]
    @techreport{wint17acos,
    author = {Steffen Moritz and Thomas Bartz-Beielstein and Jan Strohschein and Ralf Seger and Dimitri Gross},
    date-added = {2017-08-17 10:48:34 +0000},
    date-modified = {2021-07-23 21:36:51 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    number = {4/2017},
    title = {Trinkwasser-Sicherheit mit Predictive Analytics und Oracle},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4869},
    year = {2017},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4869}}
  • [PDF] [DOI] Steffen Moritz and Thomas Bartz-Beielstein. imputeTS: Time Series Missing Value Imputation in R. The R Journal, 9(1):207–218, 2017.
    [Bibtex]
    @article{mori17a,
    author = {Steffen Moritz and Thomas Bartz-Beielstein},
    date-added = {2021-07-22 12:01:58 +0200},
    date-modified = {2021-07-22 12:03:09 +0200},
    doi = {10.32614/RJ-2017-009},
    journal = {{The R Journal}},
    keywords = {bartzPublic},
    number = {1},
    pages = {207--218},
    title = {{imputeTS: Time Series Missing Value Imputation in R}},
    url = {https://doi.org/10.32614/RJ-2017-009},
    volume = {9},
    year = {2017},
    bdsk-file-1 = {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},
    bdsk-url-1 = {https://doi.org/10.32614/RJ-2017-009}}
  • [PDF] Margarita Alejandra C. Rebolleco, Oscar Manuel V. Baez, Thomas Bartz-Beielstein, and Lars Ribbe. Bias-correction of satellite rainfall estimates through the use of metamodels using gaussian process and bayesian regression. a case study for the imperial basin (chile). In F. Hoffmann, E. Hüllermeier, and R. Mikut, editors, 27. workshop computational intelligence, pages 261-278, 2017.
    [Bibtex]
    @inproceedings{Rebo17a,
    author = {Rebolleco, Margarita Alejandra C. and Baez, Oscar Manuel V. and Bartz-Beielstein, Thomas and Ribbe, Lars},
    booktitle = {27. Workshop Computational Intelligence},
    date-added = {2018-01-07 21:55:42 +0000},
    date-modified = {2018-01-07 22:00:58 +0000},
    editor = {F. Hoffmann and E. H{\"u}llermeier and R. Mikut},
    keywords = {bartzPublic},
    pages = {261- 278},
    title = {Bias-Correction of Satellite Rainfall Estimates Through the Use of Metamodels using Gaussian Process and Bayesian Regression. A Case Study for the Imperial Basin (Chile)},
    year = {2017},
    bdsk-file-1 = {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}}
  • [PDF] Margarita Alejandra C. Rebolleco, Oscar Manuel V. Baez, Thomas Bartz-Beielstein, and Lars Ribbe. A parametric and non-parametric metamodeling approach for the bias-correction of satellite rainfall estimates using rain gauge measurements. cases of study: magdalena basin (colombia), imperial basin (chile) and paraiba do sul (brazil).. Abstract h34f-08 agu fall meeting 2017, 2017.
    [Bibtex]
    @article{Rebo17b,
    author = {Rebolleco, Margarita Alejandra C. and Baez, Oscar Manuel V. and Bartz-Beielstein, Thomas and Ribbe, Lars},
    date-added = {2018-01-07 21:53:22 +0000},
    date-modified = {2018-01-07 21:54:50 +0000},
    journal = {Abstract H34F-08 AGU Fall meeting 2017},
    keywords = {bartzPublic},
    title = {A parametric and non-parametric metamodeling approach for the bias-correction of Satellite Rainfall Estimates using rain gauge measurements. Cases of study: Magdalena Basin (Colombia), Imperial Basin (Chile) and Paraiba do Sul (Brazil).},
    year = {2017},
    bdsk-file-1 = {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}}
  • [PDF] Alexis Sardá-Espinosa, Subanatarajan Subbiah, and Thomas Bartz-Beielstein. Conditional inference trees for knowledge extraction from motor health condition data. CIplus Report 1/2017, TH Köln, April 2017.
    [Bibtex]
    @techreport{Sard16acos,
    abstract = {Abstract Computational tools for the analysis of data gathered by monitoring systems are necessary because the amount of data steadily increases. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.},
    author = {Sard{\'a}-Espinosa, Alexis and Subbiah, Subanatarajan and Bartz-Beielstein, Thomas},
    date-added = {2018-07-02 13:06:55 +0000},
    date-modified = {2021-07-23 21:41:24 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction},
    month = {April},
    number = {1/2017},
    title = {Conditional inference trees for knowledge extraction from motor health condition data},
    type = {CIplus Report},
    year = {2017},
    bdsk-file-1 = {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}}
  • [DOI] Alexis Sardá-Espinosa, Subanatarajan Subbiah, and Thomas Bartz-Beielstein. Conditional inference trees for knowledge extraction from motor health condition data. Engineering applications of artificial intelligence, 62:26–37, 6 2017.
    [Bibtex]
    @article{Sard16a,
    abstract = {Abstract Computational tools for the analysis of data gathered by monitoring systems are necessary because the amount of data steadily increases. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.},
    author = {Sard{\'a}-Espinosa, Alexis and Subbiah, Subanatarajan and Bartz-Beielstein, Thomas},
    date = {2017/6//},
    date-added = {2017-03-31 21:08:43 +0000},
    date-modified = {2017-03-31 21:09:47 +0000},
    doi = {http://dx.doi.org/10.1016/j.engappai.2017.03.008},
    isbn = {0952-1976},
    journal = {Engineering Applications of Artificial Intelligence},
    keywords = {bartzPublic, Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction},
    month = {6},
    pages = {26--37},
    title = {Conditional inference trees for knowledge extraction from motor health condition data},
    ty = {JOUR},
    url = {http://www.sciencedirect.com/science/article/pii/S0952197617300532},
    volume = {62},
    year = {2017},
    bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0952197617300532},
    bdsk-url-2 = {http://dx.doi.org/10.1016/j.engappai.2017.03.008}}
  • [PDF] Jörg Stork and Thomas Bartz-Beielstein. Global optimization strategies: analogies to human behavior. In Id at nrw’18, 2017.
    [Bibtex]
    @inproceedings{Stor17d,
    author = {Stork, J{\"o}rg and Bartz-Beielstein, Thomas},
    booktitle = {ID at NRW'18},
    date-added = {2018-02-10 17:40:15 +0000},
    date-modified = {2018-12-02 18:36:09 +0100},
    keywords = {bartzPublic},
    title = {Global Optimization Strategies: Analogies to Human Behavior},
    year = {2017},
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  • [PDF] Jörg Stork, Thomas Bartz-Beielstein, Andreas Fischbach, and Martin Zaefferer. Surrogate assisted learning of neural networks. In Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, editors, Gma ci-workshop 2017, pages 195-210, 2017.
    [Bibtex]
    @inproceedings{Stor17c,
    author = {J{\"o}rg Stork and Thomas Bartz-Beielstein and Andreas Fischbach and Martin Zaefferer},
    booktitle = {GMA CI-Workshop 2017},
    date-added = {2018-01-07 22:05:44 +0000},
    date-modified = {2018-01-07 22:07:22 +0000},
    editor = {Frank Hoffmann and Eyke H{\"u}llermeier and Ralf Mikut},
    keywords = {bartzPublic, public},
    pages = {195-210},
    title = {Surrogate Assisted Learning of Neural Networks},
    year = {2017},
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  • [PDF] [DOI] Martin Zaefferer, Andreas Fischbach, Boris Naujoks, and Thomas Bartz-Beielstein. Simulation-based test functions for optimization algorithms. In Proceedings of the genetic and evolutionary computation conference, GECCO ’17, page 905–912, New York, NY, USA, 2017. Acm.
    [Bibtex]
    @inproceedings{Zaef17a,
    acmid = {3071190},
    address = {New York, NY, USA},
    author = {Zaefferer, Martin and Fischbach, Andreas and Naujoks, Boris and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
    date-added = {2017-09-14 20:19:19 +0000},
    date-modified = {2017-09-14 20:19:50 +0000},
    doi = {10.1145/3071178.3071190},
    isbn = {978-1-4503-4920-8},
    keywords = {modeling, optimization, simulation, test function generator,bartzPublic},
    location = {Berlin, Germany},
    numpages = {8},
    pages = {905--912},
    publisher = {ACM},
    series = {GECCO '17},
    title = {Simulation-based Test Functions for Optimization Algorithms},
    url = {http://doi.acm.org/10.1145/3071178.3071190},
    year = {2017},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/3071178.3071190},
    bdsk-url-2 = {http://dx.doi.org/10.1145/3071178.3071190}}
  • [PDF] Martin Zaefferer, Andreas Fischbach, Boris Naujoks, and Thomas Bartz-Beielstein. Simulation-based test functions for optimization algorithms. CIplus Report 3/2017, Fakultät für Informatik und Ingenieurwissenschaften (F10), TH Köln, 2017.
    [Bibtex]
    @techreport{zaef17acos,
    address = {TH K{\"o}ln},
    author = {Martin Zaefferer and Andreas Fischbach and Boris Naujoks and Thomas Bartz-Beielstein},
    date-added = {2017-07-07 12:48:25 +0000},
    date-modified = {2021-07-23 21:48:59 +0200},
    institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
    keywords = {bartzPublic},
    number = {3/2017},
    pages = {12},
    series = {CIplus},
    title = {Simulation-based Test Functions for Optimization Algorithms},
    type = {CIplus Report},
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    year = {2017},
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    bdsk-url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4777}}

2016

  • [PDF] Thomas Bartz-Beielstein. A Survey of Model-Based Methods for Global Optimization. In Gregor Papa and Marjan Mernik, editors, Bioinspired optimization methods and their applications, page 1–18, 2016. Video Lectures: http://videolectures.net/bioma2016_bartz_beielstein_based_methods/
    [Bibtex]
    @inproceedings{Bart16c,
    abstract = {This article describes model-based methods for global optimization. After introducing the global optimization framework, modeling approaches for stochastic algorithms are presented. We differentiate between models that use a distribution and models that use an explicit surrogate model. Fundamental aspects of and recent advances in surrogate-model based optimization are discussed. Strategies for selecting and evaluat- ing surrogates are presented. The article concludes with a description of key features of two state-of-the-art surrogate model based algorithms, namely the evolvability learning of surrogates (EvoLS) algorithm and the sequential parameter optimization (SPO).},
    author = {Bartz-Beielstein, Thomas},
    booktitle = {Bioinspired Optimization Methods and their Applications},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2021-07-23 21:57:34 +0200},
    editor = {Papa, Gregor and Mernik, Marjan},
    groups = {bartzPublic},
    keywords = {Surrogate, Bart16n, Bart16e, bartzPublic, free},
    month = may,
    note = {Video Lectures: http://videolectures.net/bioma2016\_bartz\_beielstein\_based\_methods/},
    pages = {1--18},
    rating = {0},
    timestamp = {2016-10-22},
    title = {{A Survey of Model-Based Methods for Global Optimization}},
    year = {2016},
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    bdsk-url-1 = {http://bioma.ijs.si/proceedings/2016/01%20-%20A%20Survey%20of%20Model-Based%20Methods%20for%20Global%20Optimization.pdf}}
  • [PDF] Thomas Bartz-Beielstein and Martin Zaefferer. Model-based methods for continuous and discrete global optimization. Schriftenreihe CIplus 8/2016, Fakultät für Informatik und Ingenieurwissenschaften (F10), TH Köln, August 2016.
    [Bibtex]
    @techreport{Bart16ncos,
    abstract = {The use of surrogate models is a standard method to deal with complex, real- world optimization problems. The first surrogate models were applied to con- tinuous optimization problems. In recent years, surrogate models gained impor- tance for discrete optimization problems. This article, which consists of three parts, takes care of this development. The first part presents a survey of model- based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part provides details for the case of discrete optimization problems. Here, six strategies for dealing with discrete data structures are in- troduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in both application domains. Keywords: Surrogate, Discrete Optimization, Combinatorial Optimization, Metamodels, Machine learning, Expensive optimization problems, Model management, Evolutionary computation},
    address = {TH K{\"o}ln},
    author = {Thomas Bartz-Beielstein and Martin Zaefferer},
    date-added = {2016-11-29 16:21:19 +0000},
    date-modified = {2017-06-04 11:57:45 +0000},
    howpublished = {Cologne Open Science},
    institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
    keywords = {bartzPublic, free},
    month = {August},
    number = {8/2016},
    pages = {54},
    series = {CIplus},
    title = {Model-based Methods for Continuous and Discrete Global Optimization},
    type = {Schriftenreihe CIplus},
    url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4356},
    year = {2016},
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    bdsk-url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4356}}
  • [PDF] Thomas Bartz-Beielstein. Stacked Generalization of Surrogate Models – A Practical Approach. CIplus Report 5/2016, TH Köln, Köln, 2016. https://cos.bibl.th-koeln.de/frontdoor/index/index/docId/375
    [Bibtex]
    @techreport{Bart16jcos,
    abstract = {This report presents a practical approach to stacked generalization in surrogate model based optimization. It exemplifies the integration of stacking methods into the surrogate model building process. First, a brief overview of the current state in surrogate model based opti- mization is presented. Stacked generalization is introduced as a promising ensemble surrogate modeling approach. Then two examples (the first is based on a real world application and the second on a set of artificial test functions) are presented. These examples clearly illustrate two properties of stacked generalization: (i) combining information from two poor performing models can result in a good performing model and (ii) even if the ensemble contains a good performing model, combining its information with information from poor performing models results in a relatively small performance decrease only.
    },
    address = {K{\"o}ln},
    affiliation = {TH K{\"o}ln},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2021-07-25 21:48:45 +0200},
    groups = {bartzPublic},
    institution = {TH K{\"o}ln},
    keywords = {Bart16n, Bart16e, bartzPublic, free},
    note = {https://cos.bibl.th-koeln.de/frontdoor/index/index/docId/375},
    number = {5/2016},
    publisher = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
    rating = {0},
    read = {Yes},
    timestamp = {2016-10-19},
    title = {{Stacked Generalization of Surrogate Models - A Practical Approach}},
    type = {CIplus Report},
    year = {2016},
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    bdsk-url-1 = {urn:nbn:de:hbz:832-cos4-3759}}
  • [PDF] Thomas Bartz-Beielstein. EASD-Experimental Algorithmics for Streaming Data. CIplus Report 2/2016, TH Köln, 2016.
    [Bibtex]
    @techreport{Bart16fcos,
    abstract = {This paper proposes an experimental methodology for on-line machine learning algorithms, i.e., for algorithms that work on data that are available in a sequential order. It is demonstrated how established tools from experimental algorithmics (EA) can be applied in the on-line or streaming data setting. The massive on-line analysis (MOA) framework is used to perform the experiments. Benefits of a well-defined report structure are discussed. The application of methods from the EA community to on-line or streaming data is referred to as experimental algorithmics for streaming data (EADS).},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2017-01-06 10:41:16 +0000},
    date-modified = {2021-07-23 22:01:03 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    number = {2/2016},
    title = {{EASD-Experimental Algorithmics for Streaming Data}},
    type = {CIplus Report},
    year = {2016},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Horst Stenzel, Martin Zaefferer, Beate Breiderhoff, Quoc Cuong Pham, Dimitri Gusew, Aylin Mengi, Baris Kabacali, Jerome Tünte, Lukas Büscher, Sascha Wüstlich, and Thomas Friesen. Optimization of the cyclone separator geometry via multimodel simulation. CIplus Report 9/2016, TH Köln, 2016.
    [Bibtex]
    @techreport{Bart16eCos,
    author = {Thomas Bartz-Beielstein and Horst Stenzel and Martin Zaefferer and Beate Breiderhoff and Quoc Cuong Pham and Dimitri Gusew and Aylin Mengi and Baris Kabacali and Jerome T{\"u}nte and Lukas B{\"u}scher and Sascha W{\"u}stlich and Thomas Friesen},
    date-added = {2017-01-31 17:42:43 +0000},
    date-modified = {2021-07-23 22:00:27 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic},
    number = {9/2016},
    pages = {28},
    series = {CIplus},
    title = {Optimization of the Cyclone Separator Geometry via Multimodel Simulation},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4380},
    year = {2016},
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    bdsk-url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4380}}
  • [PDF] Thomas Bartz-Beielstein. Experimental Algorithmics Applied to On-line Machine Learning. In Gregor Papa and Marjan Mernik, editors, Bioinspired optimization methods and their applications, page 94–104, 2016.
    [Bibtex]
    @inproceedings{Bart16g,
    abstract = {The application of methods from experimental algorithmics to on-line or streaming data is referred to as experimental algorithmics for streaming data (EADS). This paper proposes an experimental methodology for on-line machine learning algorithms, i.e., for algorithms that work on data that are available in a sequential order. It is demonstrated how established tools from experimental algorithmics can be applied in the on-line or streaming data setting. The massive on-line analysis frame- work is used to perform the experiments. Benefits of a well-defined report structure are discussed.
    },
    author = {Bartz-Beielstein, Thomas},
    booktitle = {Bioinspired Optimization Methods and their Applications},
    date-added = {2016-05-03T18:01:21GMT},
    date-modified = {2017-10-15 13:00:45 +0000},
    editor = {Papa, Gregor and Mernik, Marjan},
    groups = {bartzPublic},
    keywords = {bartzPublic, free},
    pages = {94--104},
    rating = {0},
    title = {{Experimental Algorithmics Applied to On-line Machine Learning}},
    year = {2016},
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  • Thomas Bartz-Beielstein. Forschendes Lernen – vom Bachelor zur Promotion in den Ingenieurwissenschaften. In Brigitte Berendt, Andreas Fleischmann, Niclas Schaper, Birgit Szczyrba, and Johannes Wildt, editors, Neues Handbuch Hochschullehre, page 1–28. Josef raabe, 2016.
    [Bibtex]
    @incollection{Bart15q,
    abstract = {Forschendes Lernen versteht sich als ein methodisches Prinzip, das Forschungsorientierung und Verkn{\"u}pfung von Forschung und Lehre in die Studieng{\"a}nge und Lehrveranstaltungen integriert und f{\"u}r studentische Lernprozesse nutzbringend anwendet. Studierende sind dabei Teil der Scientific Community. Dieser Beitrag ist ein Erfahrungsbericht, in dem das Konzept des {\quotedblbase}Forschenden Ler- nens{\textquotedblleft} in einer Variante vorgestellt wird, die in den letzten zehn Jahren an einer deutschen Fach- hochschule f{\"u}r ingenieurwissenschaftliche Studieng{\"a}nge entwickelt wurde. Da es {\quotedblbase}das{\textquotedblleft} Forschende Lernen nicht gibt, werden zun{\"a}chst die f{\"u}r diesen Beitrag relevanten Gesichtspunkte zusammenge- stellt. Darauf aufbauend wird ein Prozessmodell des Forschenden Lernens vorgestellt. Dieses Mo- dell erm{\"o}glicht Forschendes Lernen f{\"u}r Bachelor- und Masterstudierende sowie f{\"u}r Doktorandin- nen und Doktoranden.},
    author = {Bartz-Beielstein, Thomas},
    booktitle = {{Neues Handbuch Hochschullehre}},
    date-added = {2015-11-29T01:33:58GMT},
    date-modified = {2017-03-03 11:04:09 +0000},
    editor = {Berendt, Brigitte and Fleischmann, Andreas and Schaper, Niclas and Szczyrba, Birgit and Wildt, Johannes},
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    keywords = {bartzPublic, nonfree},
    pages = {1--28},
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  • [PDF] Thomas Bartz-Beielstein. Forschendes Lernen – vom Bachelor zur Promotion in den Ingenieurwissenschaften. CIplus Report 1, TH Köln, 2016.
    [Bibtex]
    @techreport{Bart15qcos,
    abstract = {Forschendes Lernen versteht sich als ein methodisches Prinzip, das Forschungsorientierung und Verkn{\"u}pfung von Forschung und Lehre in die Studieng{\"a}nge und Lehrveranstaltungen integriert und f{\"u}r studentische Lernprozesse nutzbringend anwendet. Studierende sind dabei Teil der Scientific Community. Dieser Beitrag ist ein Erfahrungsbericht, in dem das Konzept des {\quotedblbase}Forschenden Ler- nens{\textquotedblleft} in einer Variante vorgestellt wird, die in den letzten zehn Jahren an einer deutschen Fach- hochschule f{\"u}r ingenieurwissenschaftliche Studieng{\"a}nge entwickelt wurde. Da es {\quotedblbase}das{\textquotedblleft} Forschende Lernen nicht gibt, werden zun{\"a}chst die f{\"u}r diesen Beitrag relevanten Gesichtspunkte zusammenge- stellt. Darauf aufbauend wird ein Prozessmodell des Forschenden Lernens vorgestellt. Dieses Mo- dell erm{\"o}glicht Forschendes Lernen f{\"u}r Bachelor- und Masterstudierende sowie f{\"u}r Doktorandin- nen und Doktoranden.},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2018-12-02 18:02:30 +0100},
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  • Thomas Bartz-Beielstein. Forschendes Lernen – vom Bachelor zur Promotion in den Ingenieurwissenschaften. In Sylvia Heuchemer and Birgit Szczyrba, editors, Universitas in projects, page 143–170. TH Köln, 2016.
    [Bibtex]
    @incollection{Bart16r,
    author = {Bartz-Beielstein, Thomas},
    booktitle = {universitas in projects},
    date-added = {2016-11-16 14:58:44 +0000},
    date-modified = {2017-01-14 15:10:55 +0000},
    editor = {Heuchemer, Sylvia and Szczyrba, Birgit},
    keywords = {bartzPublic, nonfree},
    pages = {143--170},
    publisher = {{TH K{\"o}ln}},
    title = {{Forschendes Lernen - vom Bachelor zur Promotion in den Ingenieurwissenschaften}},
    year = {2016}}
  • [PDF] Sowmya Chandrasekaran, Steffen Moritz, Martin Zaefferer, Jörg Stork, Thomas Bartz-Beielstein, and Thomas Bartz-Beielstein. Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs. In Frank Hoffmann and Eyke Hüllermeier, editors, Workshop computational intelligence, page 1–20, dec 2016.
    [Bibtex]
    @inproceedings{Chan16a,
    abstract = {Data pre-processing is a key research topic in data mining because it plays a crucial role in improving the accuracy of any data mining algorithm. In most real world cases, a signi cant amount of the recorded data is found missing. This loss of data is nearly always unavoidable. Reasons may be sensor errors, transmission errors, errors of the operator and many others. The accuracy of forecasting, classi cation, estimation, and pattern detection of any data mining algorithm depends signi cantly on the accuracy of data used in modeling. Hence, recovery of missing data plays a vital role in avoiding inaccurate data mining decisions. Although extensive research has already been done on multivariate imputation, most multivariate methods are not compatible to univariate datasets. Also, most of the traditional univariate imputation techniques become highly biased as the missing data gap increases. The current technological advancements enable generation of abundant data every second. Hence, we intend to develop a new algorithm that enables maximum utilization of the available big datasets for imputation. In this paper, we present a Seasonal and Trend decomposition using Loess (STL) based Seasonal Moving Window Algorithm, which is capable of handling patterns with trend as well as cyclic characteristics. The performance of this algorithm is evaluated with a large industrial dataset and with several datasets with well-known characteristics. We show that the algorithm is well suited to work with various kinds of univariate datasets and is highly suitable for pre-processing of large datasets.},
    author = {Chandrasekaran, Sowmya and Moritz, Steffen and Zaefferer, Martin and Stork, J{\"o}rg and Bartz-Beielstein, Thomas and Bartz-Beielstein, Thomas},
    booktitle = {Workshop Computational Intelligence},
    date-added = {2016-09-25T18:39:03GMT},
    date-modified = {2017-03-07 09:16:04 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    groups = {bartzPublic},
    keywords = {Sard16a, bartzPublic, free},
    month = dec,
    pages = {1--20},
    rating = {0},
    read = {Yes},
    title = {{Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs}},
    year = {2016},
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  • [DOI] Carola Doerr, Nicolas Bredeche, Enrique Alba, Thomas Bartz-Beielstein, Dimo Brockhoff, Benjamin Doerr, Gusz Eiben, Michael G. Epitropakis, Carlos M. Fonseca, Andreia Guerreiro, Evert Haasdijk, Jacqueline Heinerman, Julien Hubert, Per Kristian Lehre, Luigi Malagò, J. J. Merelo, Julian Miller, Boris Naujoks, Pietro Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Patricia Ryser-Welch, Giovanni Squillero, Jörg Stork, Dirk Sudholt, Alberto Tonda, Darrell Whitley, and Martin Zaefferer. Tutorials at ppsn 2016. In Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, and Ben Paechter, editors, Parallel problem solving from nature – ppsn xiv: 14th international conference, edinburgh, uk, september 17-21, 2016, proceedings, page 1012–1022. Springer international publishing, 2016.
    [Bibtex]
    @incollection{Doer16a,
    author = {Doerr, Carola and Bredeche, Nicolas and Alba, Enrique and Bartz-Beielstein, Thomas and Brockhoff, Dimo and Doerr, Benjamin and Eiben, Gusz and Epitropakis, Michael G. and Fonseca, Carlos M. and Guerreiro, Andreia and Haasdijk, Evert and Heinerman, Jacqueline and Hubert, Julien and Lehre, Per Kristian and Malag{\`o}, Luigi and Merelo, J. J. and Miller, Julian and Naujoks, Boris and Oliveto, Pietro and Picek, Stjepan and Pillay, Nelishia and Preuss, Mike and Ryser-Welch, Patricia and Squillero, Giovanni and Stork, J{\"o}rg and Sudholt, Dirk and Tonda, Alberto and Whitley, Darrell and Zaefferer, Martin},
    booktitle = {Parallel Problem Solving from Nature -- PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
    date-added = {2017-02-14 11:28:00 +0000},
    date-modified = {2018-01-07 20:47:43 +0000},
    doi = {10.1007/978-3-319-45823-6\_95},
    editor = {Handl, Julia and Hart, Emma and Lewis, Peter R. and L{\'o}pez-Ib{\'a}{\~{n}}ez, Manuel and Ochoa, Gabriela and Paechter, Ben},
    isbn = {978-3-319-45823-6},
    keywords = {bartzPublic, nonfree},
    pages = {1012--1022},
    publisher = {Springer International Publishing},
    title = {Tutorials at PPSN 2016},
    url = {http://dx.doi.org/10.1007/978-3-319-45823-6\_95},
    year = {2016},
    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-319-45823-6_95},
    bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-45823-6%5C_95}}
  • [PDF] Andreas Fischbach, Martin Zaefferer, Jörg Stork, Martina Friese, and Thomas Bartz-Beielstein. From real world data to test functions. In Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, editors, Proceedings. 26. workshop computational intelligence, page 159–177, Dortmund, 2016. Kit scientific publishing.
    [Bibtex]
    @inproceedings{Fisc16a,
    abstract = {When researchers and practitioners in the field of computational intelligence are confronted with real-world problems, the question arises which method is the best to apply. Nowadays, there are several, well established test suites and well known artificial benchmark functions available. However, relevance and applicability of these methods to real-world problems remains an open question in many situations. Furthermore, the generalizability of these methods cannot be taken for granted. Some preliminary ideas about generalizability are discussed in [1, 2].
    This paper describes a data-driven approach for the generation of test instances, based on real-world data, as depicted in Figure 1. The test instance generation uses data-preprocessing, feature extraction, modeling, and parameterization. It was applied to several real-world scenarios, e.g., in the context of genetic programming [3]. In this work we apply this concept on a classical design of experiment real-world project and generate test instances for benchmarking, i.e., design of experiment methods and model fitness. But it can also be used to compare and analyze several surrogate techniques and optimization algorithms as well.
    In most cases, complex and expensive real-world problems do not provide su cient data for comparison of methods. Thus, our goal is our goal is to create a toolbox containing multiple data sets of real-world projects. With that toolbox, researchers are granted access to both the data sets and the derived test functions.},
    address = {Dortmund},
    author = {Andreas Fischbach and Martin Zaefferer and J{\"o}rg Stork and Martina Friese and Thomas Bartz-Beielstein},
    booktitle = {Proceedings. 26. Workshop Computational Intelligence},
    date-added = {2017-02-14 11:12:35 +0000},
    date-modified = {2017-03-07 21:40:36 +0000},
    editor = {Frank Hoffmann and Eyke H{\"u}llermeier and Ralf Mikut},
    keywords = {bartzPublic, free},
    pages = {159--177},
    publisher = {KIT Scientific Publishing},
    title = {From Real World Data to Test Functions},
    year = {2016},
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  • [PDF] Martina Friese, Thomas Bartz-Beielstein, and Michael Emmerich. Building Ensembles of Surrogate Models by Optimal Convex Combination (Preprint). CIplus Report 4/2016, TH Köln, Cologne, 2016.
    [Bibtex]
    @techreport{Frie16acos,
    address = {Cologne},
    author = {Friese, Martina and Bartz-Beielstein, Thomas and Emmerich, Michael},
    date-added = {2016-06-02T19:42:34GMT},
    date-modified = {2021-07-23 22:04:52 +0200},
    groups = {bartzPublic},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    month = apr,
    number = {4/2016},
    rating = {0},
    timestamp = {2016-10-19},
    title = {{Building Ensembles of Surrogate Models by Optimal Convex Combination (Preprint)}},
    type = {CIplus Report},
    year = {2016},
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  • [PDF] Martina Friese, Thomas Bartz-Beielstein, and Michael T. M. Emmerich. Building ensembles of surrogates by optimal convex combination. In Marjan Mernik and Gregor Papa, editors, Bioinspired optimization methods and their applications, page 131–144, may 2016.
    [Bibtex]
    @inproceedings{Frie16b,
    abstract = {When using machine learning techniques for learning a function approx- imation from given data it can be difficult to select the right modelling technique. Without preliminary knowledge about the function it might be beneficial if the algorithm could learn all models by itself and select the model that suits best to the problem, an approach known as auto- mated model selection. We propose a generalization of this approach that also allows to combine the predictions of several surrogate mod- els into one more accurate ensemble surrogate model. This approach is studied in a fundamental way, by first evaluating minimalistic en- sembles of only two surrogate models in detail and then proceeding to ensembles with more surrogate models. The results show to what ex- tent combinations of models can perform better than single surrogate models and provide insights into the scalability and robustness of the approach. The focus is on multi-modal functions which are important in surrogate-assisted global optimization.
    },
    author = {Friese, Martina and Bartz-Beielstein, Thomas and Emmerich, Michael T M},
    booktitle = {Bioinspired Optimization Methods and their Applications},
    date-added = {2016-05-03T17:42:54GMT},
    date-modified = {2017-01-14 15:24:12 +0000},
    editor = {Mernik, Marjan and Papa, Gregor},
    groups = {bartzPublic},
    keywords = {bartzPublic, free},
    month = may,
    pages = {131--144},
    rating = {0},
    title = {{Building ensembles of surrogates by optimal convex combination}},
    year = {2016},
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  • [PDF] Sebastian Krey, Margarita Rebolledo A. Coy, Dimitri Gusew, Viktoria Schaale, Jörg Stork, and Thomas Bartz-Beielstein. Modeling and calibration of robust gas sensors. In Axel Fürstberger, Ludwig Lausser, Johann Kraus, Matthias Schmid, and Hans Kestler, editors, 48. arbeitstagung der arbeitsgruppen statistical computing (gmds/ibs-dr) und klassifikation und datenanalyse in den biowissenschaften (gfkl), number 4 in Ulmer Informatik-Berichte, Günzburg, 7 2016. Open Access Repositorium der Universität Ulm.
    [Bibtex]
    @inproceedings{Krey16b,
    address = {G{\"u}nzburg},
    author = {Sebastian Krey and Margarita A. Rebolledo Coy and Dimitri Gusew and Viktoria Schaale and J{\"o}rg Stork and Thomas Bartz-Beielstein},
    booktitle = {48. Arbeitstagung der Arbeitsgruppen Statistical Computing (GMDS/IBS-DR) und Klassifikation und Datenanalyse in den Biowissenschaften (GfKl)},
    date-added = {2017-03-09 14:17:31 +0000},
    date-modified = {2018-01-07 21:28:37 +0000},
    editor = {Axel F{\"u}rstberger and Ludwig Lausser and Johann Kraus and Matthias Schmid and Hans Kestler},
    keywords = {bartzPublic, public},
    month = {7},
    note = {Open Access Repositorium der Universit{\"a}t Ulm.},
    number = {4},
    series = {Ulmer Informatik-Berichte},
    title = {Modeling and Calibration of Robust Gas Sensors},
    year = {2016},
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  • [PDF] Boris Naujoks, Jörg Stork, Martin Zaefferer, and Thomas Bartz-Beielstein. Presentation Slides: Meta-Model Assisted Evolutionary Optimization. Tutorial at PPSN 2016. In Parallel problem solving from nature, page 1–104, sep 2016.
    [Bibtex]
    @inproceedings{Nauj16a,
    author = {Naujoks, Boris and Stork, J{\"o}rg and Zaefferer, Martin and Bartz-Beielstein, Thomas},
    booktitle = {Parallel Problem Solving from Nature},
    date-added = {2016-09-27T05:11:11GMT},
    date-modified = {2017-06-10 17:00:03 +0000},
    keywords = {bartzPublic, BartzTutorial, free},
    month = sep,
    pages = {1--104},
    rating = {0},
    read = {Yes},
    title = {{Presentation Slides: Meta-Model Assisted Evolutionary Optimization. Tutorial at PPSN 2016}},
    year = {2016},
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  • [PDF] Margarita Alejandra Rebolledo Coy, Sebastian Krey, Thomas Bartz-Beielstein, Oliver Flasch, Andreas Fischbach, and Jörg Stork. Modeling and optimization of a robust gas sensor. In Gregor Papa and Marjan Mernik, editors, Bioinspired optimization methods and their applications, page 267–278, 2016.
    [Bibtex]
    @inproceedings{Rebo15a,
    abstract = {In this paper we present a comparison of different data driven modeling methods. The first instance of a data driven linear Bayesian model is compared with several linear regression models, a Kriging model and a genetic programming model. The models are build on industrial data for the development of a robust gas sensor. The data contain limited amount of samples and a high variance. The mean square error of the models implemented in a test dataset is used as the comparison strategy. The results indicate that standard linear regression approaches as well as Kriging and GP show good results, whereas the Bayesian approach, despite the fact that it requires additional resources, does not lead to improved results.},
    author = {Rebolledo Coy, Margarita Alejandra and Krey, Sebastian and Bartz-Beielstein, Thomas and Flasch, Oliver and Fischbach, Andreas and Stork, J{\"o}rg},
    booktitle = {Bioinspired Optimization Methods and their Applications},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2017-01-14 15:29:41 +0000},
    editor = {Papa, Gregor and Mernik, Marjan},
    keywords = {Bart16n, bartzPublic, free},
    pages = {267--278},
    rating = {0},
    title = {Modeling and Optimization of a Robust Gas Sensor},
    year = {2016},
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  • [PDF] Margarita Alejandra Rebolledo Coy, Sebastian Krey, Thomas Bartz-Beielstein, Oliver Flasch, Andreas Fischbach, and Jörg Stork. Modeling and Optimization of a Robust Gas Sensor. Technical Report 03/2016, Cologne Open Science, Cologne, 2016.
    [Bibtex]
    @techreport{Rebo15acos,
    abstract = {In this paper we present a comparison of different data driven modeling methods. The first instance of a data driven linear Bayesian model is compared with several linear regression models, a Kriging model and a genetic programming model. The models are build on industrial data for the development of a robust gas sensor. The data contain limited amount of samples and a high variance. The mean square error of the models implemented in a test dataset is used as the comparison strategy. The results indicate that standard linear regression approaches as well as Kriging and GP show good results, whereas the Bayesian approach, despite the fact that it requires additional resources, does not lead to improved results.},
    address = {Cologne},
    affiliation = {Cologne Open Science},
    author = {Rebolledo Coy, Margarita Alejandra and Krey, Sebastian and Bartz-Beielstein, Thomas and Flasch, Oliver and Fischbach, Andreas and Stork, J{\"o}rg},
    date-added = {2016-03-12T22:36:23GMT},
    date-modified = {2017-03-03 10:54:11 +0000},
    institution = {Cologne Open Science},
    keywords = {bartzPublic, free},
    number = {03/2016},
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    timestamp = {2016-10-26},
    title = {{Modeling and Optimization of a Robust Gas Sensor}},
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  • [PDF] [DOI] Martin Zaefferer, Daniel Gaida, and Thomas Bartz-Beielstein. Multi-fidelity modeling and optimization of biogas plants. Applied soft computing, 48:13–28, nov 2016.
    [Bibtex]
    @article{Zaef13b,
    abstract = {An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. For this purpose, accurate simulation models are mandatory, because the underlying biochemical processes are very slow. The simulation models may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. To this end, multi-fidelity modeling methods like Co-Kriging are applied. Furthermore, a two-layered modeling approach is used to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is only partly successful. Given the same budget of function evaluations, the multi-fidelity approach outperforms the alternatives. However, due to considerable computational requirements, this advantage may not translate into a success with regards to overall computation time.},
    author = {Zaefferer, Martin and Gaida, Daniel and Bartz-Beielstein, Thomas},
    date-added = {2016-09-18T14:25:35GMT},
    date-modified = {2017-02-14 13:19:18 +0000},
    doi = {10.1016/j.asoc.2016.05.047},
    groups = {bartzPublic},
    journal = {Applied Soft Computing},
    keywords = {bartzPublic, nonfree, bart16n},
    language = {English},
    month = nov,
    pages = {13--28},
    rating = {0},
    read = {Yes},
    title = {{Multi-fidelity modeling and optimization of biogas plants}},
    url = {http://www.sciencedirect.com/science/article/pii/S1568494616302575},
    volume = {48},
    year = {2016},
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    bdsk-url-2 = {http://dx.doi.org/10.1016/j.asoc.2016.05.047}}
  • [PDF] [DOI] Martin Zaefferer and Thomas Bartz-Beielstein. Efficient global optimization with indefinite kernels. In Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, and Ben Paechter, editors, Parallel problem solving from nature – ppsn xiv: 14th international conference, edinburgh, uk, september 17-21, 2016, proceedings, pages 69-79. Springer international publishing, Cham, 2016.
    [Bibtex]
    @incollection{Zaef16b,
    abstract = {Kernel based surrogate models like Kriging are a popular remedy for costly objective function evaluations in optimization. Often, kernels are required to be definite. Highly customized kernels, or kernels for combinatorial representations, may be indefinite. This study investi- gates this issue in the context of Kriging. It is shown that approaches from the field of Support Vector Machines are useful starting points, but require further modifications to work with Kriging. This study compares a broad selection of methods for dealing with indefinite kernels in Krig- ing and Kriging-based E cient Global Optimization, including spectrum transformation, feature embedding and computation of the nearest defi- nite matrix. Model quality and optimization performance are tested. The standard, without explicitly correcting indefinite matrices, yields func- tional results, which are further improved by spectrum transformations.},
    address = {Cham},
    author = {Zaefferer, Martin and Bartz-Beielstein, Thomas},
    booktitle = {Parallel Problem Solving from Nature -- PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
    date-added = {2017-02-14 11:35:11 +0000},
    date-modified = {2019-05-12 12:12:26 +0200},
    doi = {10.1007/978-3-319-45823-6_7},
    editor = {Handl, Julia and Hart, Emma and Lewis, Peter R. and L{\'o}pez-Ib{\'a}{\~{n}}ez, Manuel and Ochoa, Gabriela and Paechter, Ben},
    isbn = {978-3-319-45823-6},
    keywords = {bartzPublic, nonfree, bart16n},
    pages = {69-79},
    publisher = {Springer International Publishing},
    title = {Efficient Global Optimization with Indefinite Kernels},
    url = {http://dx.doi.org/10.1007/978-3-319-45823-6_7},
    year = {2016},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-319-45823-6_7}}

2015

  • [PDF] Thomas Bartz-Beielstein and Martin Zaefferer. CIMO – CI-basierte Mehrkriterielle Optimierungsverfahren für Anwendungen in der Industrie (Schlussbericht). Technical Report, Fachhochschule Köln, Fakultät für Informatik und Ingenieurwissenschaften, 2015.
    [Bibtex]
    @techreport{Zaef15c,
    author = {Thomas Bartz-Beielstein and Martin Zaefferer},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2021-07-23 22:27:14 +0200},
    institution = {Fachhochschule K{\"o}ln, Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften},
    keywords = {Bart16n, bartzPublic, free},
    timestamp = {2015.03.20},
    title = {{CIMO - CI-basierte Mehrkriterielle Optimierungsverfahren f{\"u}r Anwendungen in der Industrie (Schlussbericht)}},
    year = {2015},
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  • [PDF] Thomas Bartz-Beielstein and Martin Zaefferer. CIMO – CI-basierte Mehrkriterielle Optimierungsverfahren für Anwendungen in der Industrie (Schlussbericht). Technical Report 5, Fachhochschule Köln, Fakultät für Informatik und Ingenieurwissenschaften, TH Köln, 2015.
    [Bibtex]
    @techreport{Zaef15ccos,
    address = {TH K{\"o}ln},
    author = {Thomas Bartz-Beielstein and Martin Zaefferer},
    date-added = {2016-11-05 18:52:40 +0000},
    date-modified = {2017-03-03 10:53:14 +0000},
    institution = {Fachhochschule K{\"o}ln, Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften},
    keywords = {Bart16n, bartzPublic},
    number = {5},
    timestamp = {2015.03.20},
    title = {{CIMO - CI-basierte Mehrkriterielle Optimierungsverfahren f{\"u}r Anwendungen in der Industrie (Schlussbericht)}},
    year = {2015},
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  • [DOI] Thomas Bartz-Beielstein. How to Create Generalizable Results. In Janusz Kacprzyk and Witold Pedrycz, editors, Springer handbook of computational intelligence, page 1127–1142. Springer berlin heidelberg, Berlin, Heidelberg, 2015.
    [Bibtex]
    @incollection{Bart15s,
    abstract = {Basically, this chapter tries to find answers for the following fundamental questions in experimental research.
    (Q-1)
    How can problem instances be generated?
    (Q-2)
    How can experimental results be generalized?
    The chapter is structured as follows. Section 56.2 introduces real-world and artificial optimization problems. Algorithms are described in Sect. 56.3. Objective functions and statistical models are introduced in Sect. 56.4; these models take problem and algorithm features into consideration. Section 56.5 presents case studies that illustrate our methodology. The chapter closes with a summary and an outlook.},
    address = {Berlin, Heidelberg},
    author = {Bartz-Beielstein, Thomas},
    booktitle = {Springer Handbook of Computational Intelligence},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2017-03-07 21:50:03 +0000},
    doi = {10.1007/978-3-662-43505-2_56},
    editor = {Kacprzyk, Janusz and Pedrycz, Witold},
    isbn = {978-3-662-43504-5},
    keywords = {Bart16n, bartzPublic, nonfree},
    language = {English},
    pages = {1127--1142},
    publisher = {Springer Berlin Heidelberg},
    rating = {0},
    title = {{How to Create Generalizable Results}},
    url = {http://dx.doi.org/10.1007/978-3-662-43505-2_56},
    year = {2015},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-662-43505-2_56}}
  • [PDF] Thomas Bartz-Beielstein and Martin Zaefferer. MCIOP – Mehrkriterielle CI-basierte Optimierungsverfahren für den industriellen Einsatz (Schlussbericht). Technical Report 6/2015, 2015.
    [Bibtex]
    @techreport{Bart15x,
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin},
    date-added = {2015-12-27T21:40:11GMT},
    date-modified = {2017-01-14 15:08:57 +0000},
    isbn = {2194-2870},
    keywords = {bartzPublic, free},
    number = {6/2015},
    publisher = {Fakult{\"a}t 10 / Institut f{\"u}r Informatik},
    rating = {0},
    read = {Yes},
    title = {{MCIOP - Mehrkriterielle CI-basierte Optimierungsverfahren f{\"u}r den industriellen Einsatz (Schlussbericht)}},
    year = {2015},
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  • [PDF] Thomas Bartz-Beielstein. Meaningful Problem Instances and Generalizable Results. CIplus Report 1/2015, Cologne University of Applied Science, Betzdorfer Str. 2, 50679 Köln, 02 2015.
    [Bibtex]
    @techreport{Bart15icos,
    abstract = {Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statis- tics: analysis of variance and its extension to mixed models. This chapter combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments.},
    address = {Betzdorfer Str. 2, 50679 K{\"o}ln},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:34:18GMT},
    date-modified = {2021-07-23 22:26:08 +0200},
    institution = {Cologne University of Applied Science},
    keywords = {bartzPublic, free},
    month = 02,
    number = {1/2015},
    publisher = {SPOTSeven Lab, Cologne University of Applied Sciences},
    rating = {0},
    read = {Yes},
    title = {{Meaningful Problem Instances and Generalizable Results}},
    type = {CIplus Report},
    year = {2015},
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  • [PDF] Thomas Bartz-Beielstein, Jürgen Branke, Jörn Mehnen, and Olaf Mersmann. Overview: Evolutionary Algorithms. Technical Report 2/2015, Fakultät 10 / Institut für Informatik, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2015.
    [Bibtex]
    @techreport{Bart15jcos,
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas and Branke, J{\"u}rgen and Mehnen, J{\"o}rn and Mersmann, Olaf},
    date-added = {2016-11-05 19:02:18 +0000},
    date-modified = {2017-03-03 10:56:19 +0000},
    institution = {Fakult{\"a}t 10 / Institut f{\"u}r Informatik},
    keywords = {bartzPublic, free},
    number = {2/2015},
    title = {{Overview: Evolutionary Algorithms}},
    year = {2015},
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  • [PDF] Thomas Bartz-Beielstein, Christian Jung, and Martin Zaefferer. Sequential Parameter Optimization in Noisy Environments. Technical Report 4, Cologne University of Applied Sciences, 2015.
    [Bibtex]
    @techreport{Bart15ocos,
    abstract = {Sequential Parameter Optimization is a model-based optimization methodology, which includes several techniques for handling uncertainty. Simple approaches such as sharp- ening and more sophisticated approaches such as optimal computing budget allocation are available. For many real world engineering problems, the objective function can be evaluated at different levels of fidelity. For instance, a CFD simulation might provide a very time consuming but accurate way to estimate the quality of a solution.The same solution could be evaluated based on simplified mathematical equations, leading to a cheaper but less accurate estimate. Combining these different levels of fidelity in a model-based optimization process is referred to as multi-fidelity optimization. This chapter describes uncertainty-handling techniques for meta-model based search heuristics in combination with multi-fidelity optimization. Co-Kriging is one power- ful method to correlate multiple sets of data from different levels of fidelity. For the first time, Sequential Parameter Optimization with co-Kriging is applied to noisy test functions. This study will introduce these techniques and discuss how they can be applied to real-world examples.},
    author = {Bartz-Beielstein, Thomas and Jung, Christian and Zaefferer, Martin},
    date-added = {2016-08-19T14:05:17GMT},
    date-modified = {2017-03-05 15:50:39 +0000},
    institution = {Cologne University of Applied Sciences},
    keywords = {bartzPublic, free},
    number = {4},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Sequential Parameter Optimization in Noisy Environments}},
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  • [PDF] Thomas Bartz-Beielstein. SPOTSeven Lab: Jahresbericht 2014/15. Technical Report, 2015.
    [Bibtex]
    @techreport{Bart15y,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2016-11-05 18:40:39 +0000},
    date-modified = {2017-01-14 15:09:05 +0000},
    keywords = {bartzPublic, free},
    title = {{SPOTSeven Lab: Jahresbericht 2014/15}},
    year = {2015},
    bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAgLi4vc2NpZWJvL1dlYnN0b3JlLmQvYmFydDE1eS5wZGZPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LYmFydDE1eS5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpiYXJ0MTV5LnBkZgAADgAYAAsAYgBhAHIAdAAxADUAeQAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL2JhcnQxNXkucGRmAAATAAEvAAAVAAIADP//AAAACAANABoAJABHAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAZc=}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Christian Jung, and Martin Zaefferer. Uncertainty Management Using Sequential Parameter Optimization. In Carlo Meloni and Gabriella Dellino, editors, Uncertainty management in simulation-optimization of complex systems: algorithms and applications, page 79–99. Springer, 2015.
    [Bibtex]
    @incollection{Bart13i,
    abstract = {Sequential Parameter Optimization (SPO) is a meta-model based search heuristic that combines classical and modern statistical techniques. It was originally developed for the analysis of search heuristics such as simulated annealing, particle swarm optimization and evolutionary algorithms [6]. Here, SPO itself will be used as a search heuristic, i.e., SPO is applied to the objective function directly. An introduction to the state-of-the-art R implementation of SPO, the so-called sequential parameter optimization toolbox (SPOT), is presented in [5].},
    author = {Bartz-Beielstein, Thomas and Jung, Christian and Zaefferer, Martin},
    booktitle = {Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications},
    date-added = {2017-01-14 15:04:00 +0000},
    date-modified = {2017-03-08 23:39:49 +0000},
    doi = {10.1007/978-1-4899-7547-8_4},
    editor = {Meloni, Carlo and Dellino, Gabriella},
    keywords = {bartzPublic, nonfree},
    pages = {79--99},
    publisher = {Springer},
    rating = {0},
    title = {{Uncertainty Management Using Sequential Parameter Optimization}},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/978-1-4899-7547-8_4}}
  • [PDF] Thomas Bartz-Beielstein, Christian Jung, and Martin Zaefferer. Uncertainty Management Using Sequential Parameter Optimization. CIplus Report 4/2015, TH Köln, Cologne, Germany, 2015.
    [Bibtex]
    @techreport{Bart13icos,
    address = {Cologne, Germany},
    author = {Bartz-Beielstein, Thomas and Jung, Christian and Zaefferer, Martin},
    date-added = {2017-03-05 15:12:10 +0000},
    date-modified = {2021-07-25 21:49:26 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    number = {4/2015},
    title = {{Uncertainty Management Using Sequential Parameter Optimization}},
    type = {CIplus Report},
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  • Thomas Bartz-Beielstein. Zen und die kunst der hochschullehre. Changing, (2):39–49, sep 2015.
    [Bibtex]
    @article{Bart15p,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:34:01GMT},
    date-modified = {2018-11-16 20:00:38 +0100},
    journal = {changing},
    keywords = {bartzPublic, free},
    month = sep,
    number = {2},
    pages = {39--49},
    rating = {0},
    title = {Zen und die Kunst der Hochschullehre},
    year = {2015},
    bdsk-file-1 = {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}}
  • [PDF] Andreas Fischbach, Jörg Stork, Martin Zaefferer, Sebastian Krey, and Thomas Bartz-Beielstein. Analyzing Capabilities of Latin Hypercube Designs Compared to Classical Experimental Design Methods. In Frank Hoffmann and Eyke Hüllermeier, editors, Proc. 25. workshop computational intelligence, page 255–269, nov 2015.
    [Bibtex]
    @inproceedings{Fisc15a,
    abstract = {Design of experiments (DOE) has proven to be a useful tool to optimize process or production output [1]. A design specifies which values for input parameters of an experiment are to be chosen to reach a desired output or gather a maximum amount of information.
    Many authors recommend space-filling and non-collapsing designs for determin- istic computer experiments [2, 3]. Latin hypercube designs (LHDs) are non- collapsing by default due to their creation rules. The space-filling property can be fulfilled by maximizing the minimum Euclidean distance of points. Those designs are especially useful for fitting Kriging models [4]. But it has not been proven, if they are the best choice.},
    author = {Fischbach, Andreas and Stork, J{\"o}rg and Zaefferer, Martin and Krey, Sebastian and Bartz-Beielstein, Thomas},
    booktitle = {Proc. 25. Workshop Computational Intelligence},
    date-added = {2016-09-18T15:13:50GMT},
    date-modified = {2017-03-06 22:32:31 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {bartzPublic, free},
    month = nov,
    pages = {255--269},
    rating = {0},
    title = {{Analyzing Capabilities of Latin Hypercube Designs Compared to Classical Experimental Design Methods}},
    year = {2015},
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  • [PDF] Oliver Flasch, Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, and Jürgen Branke. Learning model-ensemble policies with genetic programming. Technical Report 3/2015, TH Köln, Köln, 2015.
    [Bibtex]
    @techreport{Flas15acos,
    abstract = {We propose to apply typed Genetic Programming (GP) to the problem of finding surrogate-model ensembles for global optimization on compute-intensive target functions. In a model ensemble, base-models such as linear models, random forest models, or Kriging models, as well as pre- and post-processing methods, are combined. In theory, an opti- mal ensemble will join the strengths of its comprising base-models while avoiding their weaknesses, offering higher prediction accuracy and ro- bustness. This study defines a grammar of model ensemble expressions and searches the set for optimal ensembles via GP. We performed an extensive experimental study based on 10 different objective functions and 2 sets of base-models. We arrive at promising results, as on unseen test data, our ensembles perform not significantly worse than the best base-model.},
    address = {K{\"o}ln},
    affiliation = {TH K{\"o}ln},
    author = {Flasch, Oliver and Friese, Martina and Zaefferer, Martin and Bartz-Beielstein, Thomas and Branke, J{\"u}rgen},
    date-added = {2015-11-29T01:34:14GMT},
    date-modified = {2018-11-16 20:00:53 +0100},
    institution = {TH K{\"o}ln},
    isbn = {2194-2870},
    keywords = {bartzPublic, free},
    language = {English},
    number = {3/2015},
    rating = {0},
    title = {Learning Model-Ensemble Policies with Genetic Programming},
    year = {2015},
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    bdsk-url-1 = {http://opus.bsz-bw.de/fhk/volltexte/2015/78}}
  • [PDF] Steffen Moritz, Alexis Sarda, Thomas Bartz-Beielstein, Martin Zaefferer, and Jörg Stork. Comparison of different Methods for Univariate Time Series Imputation in R. arXiv, 2015.
    [Bibtex]
    @misc{Mori15a,
    abstract = {Missing values in datasets are a well-known problem and there are quite a lot of R packages offering imputation functions. But while imputation in general is well covered within R, it is hard to find functions for imputation of univariate time series. The problem is, most standard imputation techniques can not be applied directly. Most algorithms rely on inter-attribute correlations, while univariate time series imputation needs to employ time dependencies. This paper provides an overview of univariate time series imputation in general and an in-detail insight into the respective implementations within R packages. Furthermore, we experimentally compare the R functions on different time series using four different ratios of missing data. Our results show that either an interpolation with seasonal kalman filter from the zoo package or a linear interpolation on seasonal loess decomposed data from the forecast package were the most effective methods for dealing with missing data in most of the scenarios assessed in this paper.},
    author = {Moritz, Steffen and Sarda, Alexis and Bartz-Beielstein, Thomas and Zaefferer, Martin and Stork, J{\"o}rg},
    date-added = {2016-09-27T09:48:50GMT},
    date-modified = {2021-07-23 22:27:56 +0200},
    eprint = {1510.03924},
    eprintclass = {stat.AP},
    eprinttype = {arxiv},
    howpublished = {arXiv},
    keywords = {Sard16a, bartzPublic, free},
    month = oct,
    rating = {0},
    read = {Yes},
    title = {{Comparison of different Methods for Univariate Time Series Imputation in R}},
    url = {http://arxiv.org/abs/1510.03924},
    year = {2015},
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    bdsk-url-1 = {http://arxiv.org/abs/1510.03924}}

2014

  • [DOI] Thomas Bartz Beielstein, Jürgen Branke, Bogdan Filipic, and Jim Smith, editors. Parallel Problem Solving from Nature – PPSN XIII – 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, volume 8672 of Lecture Notes in Computer Science. Springer, Cham, 2014.
    [Bibtex]
    @book{Bart14m,
    address = {Cham},
    date-added = {2015-11-29T01:34:25GMT},
    date-modified = {2017-01-14 14:23:22 +0000},
    doi = {10.1007/978-3-319-10762-2},
    editor = {Beielstein, Thomas Bartz and Branke, J{\"u}rgen and Filipic, Bogdan and Smith, Jim},
    isbn = {978-3-319-10761-5},
    issn = {0302-9743},
    keywords = {bartzPublic, nonfree},
    publisher = {Springer},
    rating = {0},
    series = {Lecture Notes in Computer Science},
    title = {{Parallel Problem Solving from Nature - PPSN XIII - 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings}},
    url = {http://link.springer.com/book/10.1007%2F978-3-319-10762-2},
    volume = {8672},
    year = {2014},
    bdsk-url-1 = {http://link.springer.com/book/10.1007%2F978-3-319-10762-2},
    bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-10762-2}}
  • [DOI] Thomas Bartz-Beielstein, Jürgen Branke, Jörn Mehnen, and Olaf Mersmann. Evolutionary algorithms. Wiley interdisciplinary reviews: data mining and knowledge discovery, 4(3):178–195, 2014.
    [Bibtex]
    @article{Bart13j,
    abstract = {Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objectives, and dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments coming from theory. Finally, typical applications of EAs to real-world problems are shown, with special emphasis on data-mining applications. WIREs Data Mining Knowl Discov 2014, 4:178--195. doi: 10.1002/widm.1124Conflict of interest: The authors have declared no conflicts of interest for this article.For further resources related to this article, please visit the WIREs website.},
    author = {Bartz-Beielstein, Thomas and Branke, J{\"u}rgen and Mehnen, J{\"o}rn and Mersmann, Olaf},
    date-added = {2017-02-11 15:57:21 +0000},
    date-modified = {2017-02-11 15:58:50 +0000},
    doi = {10.1002/widm.1124},
    issn = {1942-4795},
    journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
    keywords = {Bart16n, bartzPublic, nonfree},
    number = {3},
    pages = {178--195},
    publisher = {John Wiley & Sons, Inc.},
    title = {Evolutionary Algorithms},
    url = {http://dx.doi.org/10.1002/widm.1124},
    volume = {4},
    year = {2014},
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    bdsk-url-1 = {http://dx.doi.org/10.1002/widm.1124}}
  • [PDF] Thomas Bartz-Beielstein and Mike Preuss. Experimental Analysis of Optimization Algorithms: Tuning and Beyond. In Yossi Borenstein and Alberto Moraglio, editors, Theory and principled methods for designing metaheuristics, page 205–245. Springer, Berlin, Heidelberg, New York, 2014.
    [Bibtex]
    @incollection{Bart11j,
    abstract = {This chapter comprises the essence of several years of tutorials the authors gave on experimental research in evolutionary computation. We highlight the renaissance of experimental techniques also in other fields to especially focus on the specific conditions of experimental research in com- puter science, or more concrete, metaheuristic optimization. The experimen- tal setup is discussed together with the pitfalls awaiting the unexperienced (and sometimes even the experienced). We present a severity criterion as a meta-statistical concept for evaluating statistical inferences, which can be used to avoid fallacies, i.e., misconceptions resulting from incorrect reasoning in argumentation caused by floor or ceiling effects. The sequential parameter optimization is discussed as a meta-statistical framework which integrates concepts such as severity. Parameter tuning is considered as a relatively new tool in method design and analysis, and it leads to the question of adapt- ability of optimization algorithms. Another branch of experimentation aims for attaining more concrete problem knowledge, we may term it `exploratory landscape analysis', containing sample and visualization techniques that are often applied but not seen as being a methodological contribution. However, this chapter is not only a renarration of well known facts. We also try a look into the future to estimate what the hot topics of methodological research will be in the next years and what changes we may expect for the whole community.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Theory and Principled Methods for Designing Metaheuristics},
    date-added = {2015-11-29T01:39:02GMT},
    date-modified = {2019-08-06 21:51:40 +0200},
    editor = {Borenstein, Yossi and Moraglio, Alberto},
    keywords = {bartzPublic, nonfree, Bart19g},
    pages = {205--245},
    publisher = {Springer},
    rating = {0},
    title = {{Experimental Analysis of Optimization Algorithms: Tuning and Beyond}},
    year = {2014},
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  • [PDF] Thomas Bartz-Beielstein. SPOTSeven Lab: Forschungsbericht 2013/14. Report, FH Köln, 2014.
    [Bibtex]
    @techreport{Bart14k,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:34:40GMT},
    date-modified = {2021-07-23 22:30:12 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{SPOTSeven Lab: Forschungsbericht 2013/14}},
    type = {Report},
    year = {2014},
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  • [PDF] Steffen Moritz, Thomas Bartz-Beielstein, Olaf Mersmann, Martin Zaefferer, and Jörg Stork. Does imputation work for improvement of domestic hot water usage prediction?. In Frank Hoffmann and Eyke Hüllermeier, editors, Proceedings. 24. workshop computational intelligence, dortmund, 27.-28. november 2014, page 205–222. Kit scientific publishing, 2014.
    [Bibtex]
    @inproceedings{Mori14a,
    abstract = {The Internet of Things - the connection of everyday objects to the inter- net - is claimed to be one of the most important future trends. More and more domestic devices like refrigerators, stoves, smoke detectors, televi- sion or even lamps are meanwhile available with integrated internet con- nectivity. However the pure ability to connect to the internet is only one part. Customers expect some extra value like smart functions from these devices. Providing these smart functions often goes along with building predictive models on the data recorded by these devices. Because of the huge amount of accruing data and occurring problems like missing data this can be quite a challenging task. Especially missing data is a quite common phenomenon, because multiple possible reasons can lead to data gaps.},
    author = {Moritz, Steffen and Bartz-Beielstein, Thomas and Mersmann, Olaf and Zaefferer, Martin and Stork, J{\"o}rg},
    booktitle = {Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27.-28. November 2014},
    date-added = {2015-11-29T01:34:29GMT},
    date-modified = {2017-03-07 09:22:25 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {bartzPublic, free},
    pages = {205--222},
    publisher = {KIT Scientific Publishing},
    rating = {0},
    title = {{Does imputation work for improvement of domestic hot water usage prediction?}},
    year = {2014},
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  • [PDF] Jörg Stork, Andreas Fischbach, Thomas Bartz-Beielstein, and Martin Zaefferer. Boosting Parameter-Tuning Efficiency with Adaptive Experimental Designs. In Frank Hoffmann and Eyke Hüllermeier, editors, Proceedings 24. workshop computational intelligence, page 223–235. Kit scientific publishing, 2014.
    [Bibtex]
    @inproceedings{Stor14a,
    abstract = {Model-based tuning has proven to be a successful method for improving the performance of computational intelligence methods such as evolution- ary algorithms or neural networks [1, 2, 3]. However, model-based tuning itself can be a demanding and time consuming task. One crucial step dur- ing the tuning process is the selection of an adequate experimental design as well as the limits of the algorithm parameter space to be explored, the so-called region of interest (ROI). In the current practice, the ROI is static, that is, chosen a priori and not changed during the tuning process.
    In this paper we will investigate adaptive ROIs. In particular, we introduce mechanisms for appropriately locating and sizing the ROI on-the-fly. We will focus on the sizing aspects, because too large ROIs may slow down the tuning process resulting in worse results. This is due to a large search space leading to a lack of detail in the most critical regions. The meta model would not be able to represent these regions adequately. By adapting the size of the ROI during the tuning process (online) this issue can be dealt with.},
    author = {Stork, J{\"o}rg and Fischbach, Andreas and Bartz-Beielstein, Thomas and Zaefferer, Martin},
    booktitle = {Proceedings 24. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:34:36GMT},
    date-modified = {2017-03-06 22:36:14 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {bartzPublic, free},
    pages = {223--235},
    publisher = {KIT Scientific Publishing},
    rating = {0},
    title = {{Boosting Parameter-Tuning Efficiency with Adaptive Experimental Designs}},
    year = {2014},
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  • [PDF] Martin Zaefferer, Jörg Stork, and Thomas Bartz-Beielstein. Distance Measures for Permutations in Combinatorial Efficient Global Optimization. In Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipic, and Jim Smith, editors, Parallel problem solving from nature–ppsn xiii, page 373–383. Springer, 2014.
    [Bibtex]
    @inproceedings{Zaef14c,
    abstract = {For expensive black-box optimization problems, surrogate- model based approaches like Efficient Global Optimization are frequently used in continuous optimization. Their main advantage is the reduction of function evaluations by exploiting cheaper, data-driven models of the actual target function. The utilization of such methods in combinatorial or mixed spaces is less common. Efficient Global Optimization and re- lated methods were extended recently to such spaces, by replacing con- tinuous distance (or similarity) measures with measures suited for the respective problem representations.
    This article investigates a larg set of distance measures for their applica- bility to various permutation problems. The main purpose is to identify, how a distance measure can be chosen, either a-priori or online. In de- tail, we show that the choice of distance measure can be integrated into the Maximum Likelihood Estimation process of the underlying Kriging model. This approach has robust, good performance, thus providing a very nice tool towards selection of a distance measure.},
    author = {Zaefferer, Martin and Stork, J{\"o}rg and Bartz-Beielstein, Thomas},
    booktitle = {Parallel Problem Solving from Nature--PPSN XIII},
    date-added = {2016-08-19T14:05:26GMT},
    date-modified = {2017-03-07 09:21:41 +0000},
    editor = {Bartz-Beielstein, Thomas and Branke, J{\"u}rgen and Filipic, Bogdan and Smith, Jim},
    keywords = {bartzPublic, nonfree},
    pages = {373--383},
    publisher = {Springer},
    rating = {0},
    title = {{Distance Measures for Permutations in Combinatorial Efficient Global Optimization}},
    year = {2014},
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  • [PDF] [DOI] Martin Zaefferer, Jörg Stork, Martina Friese, Andreas Fischbach, Boris Naujoks, and Thomas Bartz-Beielstein. Efficient Global Optimization for Combinatorial Problems. In Dirk V. Arnold, editor, Genetic and evolutionary computation conference (gecco’14), proceedings, page 871–878. Acm, 2014.
    [Bibtex]
    @inproceedings{Zaef14b,
    abstract = {Real-world optimization problems may require time consum- ing and expensive measurements or simulations. Recently, the application of surrogate model-based approaches was extended from continuous to combinatorial spaces. This ex- tension is based on the utilization of suitable distance mea- sures like Hamming or Swap Distance. In this work, such an extension is implemented for Kriging (Gaussian Process) models. Kriging provides a measure of uncertainty when determining predictions. This can be harnessed to calculate the Expected Improvement (EI) of a candidate solution. In continuous optimization, EI is used in the Efficient Global Optimization (EGO) approach to balance exploitation and exploration for expensive optimization problems. Employ- ing the extended Kriging model, we show for the first time that EGO can successfully be applied to combinatorial optimization problems. We describe necessary adaptations and arising issues as well as experimental results on several test problems. All surrogate models are optimized with a Ge- netic Algorithm (GA). To yield a comprehensive compar- ison, EGO and Kriging based approaches are compared to an earlier suggested Radial Basis Function Network, a linear modeling approach, as well as model-free optimization with random search and GA. EGO clearly outperforms the com- peting approaches on most of the tested problem instances.},
    author = {Zaefferer, Martin and Stork, J{\"o}rg and Friese, Martina and Fischbach, Andreas and Naujoks, Boris and Bartz-Beielstein, Thomas},
    booktitle = {Genetic and Evolutionary Computation Conference (GECCO'14), Proceedings},
    date-added = {2016-08-19T14:05:36GMT},
    date-modified = {2017-03-07 09:26:05 +0000},
    doi = {http://doi.acm.org/10.1145/2576768.2598282},
    editor = {Arnold, Dirk V},
    keywords = {bartzPublic, nonfree},
    pages = {871--878},
    publisher = {ACM},
    rating = {0},
    title = {{Efficient Global Optimization for Combinatorial Problems}},
    year = {2014},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/2576768.2598282}}
  • [PDF] Martin Zaefferer, Daniel Gaida, and T. Bartz-Beielstein. Multi-fidelity Simulation and Optimization of a Biogas Plant. CIplus Report 2/2014, FH Köln, 2014.
    [Bibtex]
    @techreport{Zaef13bcos,
    abstract = {An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate- model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, multi- fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms.
    Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.},
    affiliation = {TH K{\"o}ln},
    author = {Zaefferer, Martin and Gaida, Daniel and Bartz-Beielstein, T},
    date-added = {2015-11-29T01:35:45GMT},
    date-modified = {2021-07-23 22:31:49 +0200},
    institution = {FH K{\"o}ln},
    isbn = {2194-2870},
    keywords = {bartzPublic, free},
    number = {2/2014},
    rating = {0},
    title = {{Multi-fidelity Simulation and Optimization of a Biogas Plant}},
    type = {CIplus Report},
    year = {2014},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Martin Zaefferer, Beate Breiderhoff, Boris Naujoks, Martina Friese, Jörg Stork, Andreas Fischbach, Oliver Flasch, and Thomas Bartz-Beielstein. Tuning multi-objective optimization algorithms for cyclone dust separators. In Proceedings of the 2014 conference on genetic and evolutionary computation, GECCO ’14, page 1223–1230, New York, NY, USA, 2014. Acm.
    [Bibtex]
    @inproceedings{Zaef14a,
    abstract = {Cyclone separators are filtration devices frequently used in industry, e.g., to filter particles from flue gas. Optimizing the cyclone geometry is a demanding task. Accurate simulations of cyclone separators are based on time consuming computational fluid dynamics simulations. Thus, the need for exploiting cheap information from analytical, approximative models is evident. Here, we employ two multi-objective optimization algorithms on such cheap, approximative models to analyze their optimization performance on this problem. Under various limitations, we tune both algorithms with Sequential Parameter Optimization (SPO) to achieve best possible results in shortest time. The resulting optimal settings are validated with different seeds, as well as with a different approximative model for collection efficiency. Their optimal performance is compared against a model based approach, where multi-objective SPO is directly employed to optimize the cyclone model, rather than tuning the optimization algorithms. It is shown that SPO finds improved parameter settings of the concerned algorithms and performs excellently when directly used as an optimizer.},
    address = {New York, NY, USA},
    author = {Zaefferer, Martin and Breiderhoff, Beate and Naujoks, Boris and Friese, Martina and Stork, J{\"o}rg and Fischbach, Andreas and Flasch, Oliver and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the 2014 Conference on Genetic and Evolutionary Computation},
    date-added = {2017-02-14 11:20:04 +0000},
    date-modified = {2018-11-16 21:25:34 +0100},
    doi = {10.1145/2576768.2598260},
    keywords = {bartzPublic, nonfree, cyclone optimization problem, evolutionary multi-objective optimization, parameter tuning, brei14a},
    location = {Vancouver, BC, Canada},
    numpages = {8},
    pages = {1223--1230},
    publisher = {ACM},
    series = {GECCO '14},
    title = {Tuning Multi-objective Optimization Algorithms for Cyclone Dust Separators},
    year = {2014},
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    bdsk-url-1 = {http://dx.doi.org/10.1145/2576768.2598260}}

2013

  • [PDF] Thomas Bartz-Beielstein and Oliver Flasch. Fiwa – methoden der computational intelligence für vorhersagemodelle in der finanz- und wasserwirtschaft (schlussbericht). Technical Report, TH Köln, Betzdorfer Str. 2, 50679 Köln, 2013.
    [Bibtex]
    @techreport{Bart13m,
    abstract = {Dieser Schlussbericht beschreibt die im Projekt Methoden der Computational Intelligence f{\"u}r Vorhersagemodelle in der Finanz- und Wasserwirtschaft (FIWA) im Zeitraum von Juni 2009 bis einschlie{\ss}lich November 2012 erzielten Ergebnisse.},
    address = {Betzdorfer Str. 2, 50679 K{\"o}ln},
    author = {Bartz-Beielstein, Thomas and Flasch, Oliver},
    date-added = {2015-11-29T01:35:45GMT},
    date-modified = {2018-11-16 20:07:52 +0100},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    language = {German},
    publisher = {Bibliothek der Fachhochschule K{\"o}ln},
    rating = {0},
    title = {FIWA -- Methoden der Computational Intelligence f{\"u}r Vorhersagemodelle in der Finanz- und Wasserwirtschaft (Schlussbericht)},
    url = {http://opus.bsz-bw.de/fhk/volltexte/2013/46},
    year = {2013},
    bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAgLi4vc2NpZWJvL1dlYnN0b3JlLmQvYmFydDEzbS5wZGZPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LYmFydDEzbS5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpiYXJ0MTNtLnBkZgAADgAYAAsAYgBhAHIAdAAxADMAbQAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL2JhcnQxM20ucGRmAAATAAEvAAAVAAIADP//AAAACAANABoAJABHAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAZc=},
    bdsk-url-1 = {http://opus.bsz-bw.de/fhk/volltexte/2013/46}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Martin Zaefferer, and Boris Naujoks. How to create meaningful and generalizable results. In Proceeding of the fifteenth annual conference companion on genetic and evolutionary computation conference companion, page 979–1004, New York, NY, USA, 2013. Acm.
    [Bibtex]
    @inproceedings{Bart13f,
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin and Naujoks, Boris},
    booktitle = {Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion},
    date-added = {2015-11-29T01:35:56GMT},
    date-modified = {2017-01-14 15:02:51 +0000},
    doi = {10.1145/2464576.2480816},
    isbn = {978-1-4503-1964-5},
    keywords = {bartzPublic, nonfree},
    pages = {979--1004},
    publisher = {ACM},
    rating = {0},
    title = {{How to create meaningful and generalizable results}},
    year = {2013},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/2464576.2480816},
    bdsk-url-2 = {http://dx.doi.org/10.1145/2464576.2480816}}
  • [PDF] Thomas Bartz-Beielstein. Mixed Models in SPOT. CIplus Report x/2013, FH Köln, 2013.
    [Bibtex]
    @techreport{Bart13c,
    abstract = {Computational intelligence methods have gained importance in several real-world domains such as process optimization, system iden- tification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. How- ever, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world set- tings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: prob- lem generation and statistical analysis of computer experiments.},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:37:16GMT},
    date-modified = {2021-07-23 22:32:51 +0200},
    institution = {FH K{\"o}ln},
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    number = {x/2013},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Mixed Models in SPOT}},
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  • [PDF] Thomas Bartz-Beielstein. SpotSeven Broschüre 2013. Technical Report, Fachhochschule Köln, 2013.
    [Bibtex]
    @techreport{Bart13e,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:37:16GMT},
    date-modified = {2016-11-06 19:58:33 +0000},
    institution = {Fachhochschule K{\"o}ln},
    keywords = {bartzPublic},
    publisher = {FH K{\"o}ln (Cologne University of Applied Sciences)},
    rating = {0},
    title = {{SpotSeven Brosch{\"u}re 2013}},
    year = {2013},
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  • [PDF] Beate Breiderhoff, Thomas Bartz-Beielstein, Boris Naujoks, Martin Zaefferer, Andreas Fischbach, Oliver Flasch, Martina Friese, Olaf Mersmann, and Jörg Stork. Preprint: Simulation and Optimization of Cyclone Dust Separators. Report 4/2013, Fachhochschule Köln, Betzdorfer Str. 2, 50679 Köln, 2013.
    [Bibtex]
    @techreport{Brei13acos,
    abstract = {CycloneDustSeparatorsaredevicesoftenusedtofiltersolidparticles from flue gas. Such cyclones are supposed to filter as much solid particles from the carrying gas as possible. At the same time, they should only introduce a mini- mal pressure loss to the system. Hence, collection efficiency has to be maximized and pressure loss minimized. Both the collection efficiency and pressure loss are heavily influenced by the cyclones geometry. In this paper, we optimize seven geometrical parameters of an analytical cyclone model. Furthermore, noise vari- ables are introduced to the model, representing the non-deterministic structure of the real-world problem. This is used to investigate robustness and sensitivity of solutions. Both the deterministic as well as the stochastic model are optimized with an SMS-EMOA. The SMS-EMOA is compared to a single objective opti- mization algorithm. For the harder, stochastic optimization problem, a surrogate- model-supported SMS-EMOA is compared against the model-free SMS-EMOA. The model supported approach yields better solutions with the same run-time budget.},
    address = {Betzdorfer Str. 2, 50679 K{\"o}ln},
    author = {Breiderhoff, Beate and Bartz-Beielstein, Thomas and Naujoks, Boris and Zaefferer, Martin and Fischbach, Andreas and Flasch, Oliver and Friese, Martina and Mersmann, Olaf and Stork, J{\"o}rg},
    date-added = {2015-11-29T01:35:45GMT},
    date-modified = {2017-03-07 23:25:27 +0000},
    institution = {Fachhochschule K{\"o}ln},
    keywords = {Bart16e, bartzPublic, free},
    language = {English},
    local-url = {file://localhost/Users/bartz/Library/Mobile%20Documents/com~apple~CloudDocs/Papers3.d/Papers%20Library/Files/E4/E42F236B-44CD-4F99-A43B-726EC42CC405.pdf},
    number = {4/2013},
    publisher = {Bibliothek der Fachhochschule K{\"o}ln},
    rating = {0},
    title = {{Preprint: Simulation and Optimization of Cyclone Dust Separators}},
    type = {Report},
    url = {http://opus.bsz-bw.de/fhk/volltexte/2013/47},
    year = {2013},
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    bdsk-url-1 = {http://opus.bsz-bw.de/fhk/volltexte/2013/47}}
  • [PDF] Beate Breiderhoff, Thomas Bartz-Beielstein, Boris Naujoks, Martin Zaefferer, Andreas Fischbach, Oliver Flasch, Martina Friese, Olaf Mersmann, and Jörg Stork. Simulation and Optimization of Cyclone Dust Separators. In Frank Hoffmann and Eyke Hüllermeier, editors, Proceedings 23. workshop computational intelligence, page 177–195. Institut für Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut für Technologie, Kit scientific publishing, 2013.
    [Bibtex]
    @inproceedings{Brei13a,
    abstract = {The reduction of emissions from coal-fired power plants is a demanding task. Cyclone separators are frequently used devices for filtering the flue gas of such plants. They remove dispersed particles from gas. Their ad- vantages are simple structure, low costs and ease of operation. Collection efficiency and pressure loss are the two most important performance pa- rameters. They are heavily influenced by the choice of several geometri- cal design parameters, like height or diameter. This results into a Multi- Objective Optimization (MOO) problem, the so called Cyclone Optimi- zation Problem (COP). This study shows how a COP can be solved and analyzed, based on an analytical, deterministic model. Furthermore, the analytical model is extended by adding several noise variables. These ena- ble to evaluate robustness of solutions, and yield a better estimate of how noisy real-world circumstances affect the problem. Techniques like a clas- sical as well as a model-supported SMS-EMOA are used to handle the MOO problem.},
    affiliation = {Institut f{\"u}r Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut f{\"u}r Technologie},
    author = {Breiderhoff, Beate and Bartz-Beielstein, Thomas and Naujoks, Boris and Zaefferer, Martin and Fischbach, Andreas and Flasch, Oliver and Friese, Martina and Mersmann, Olaf and Stork, J{\"o}rg},
    booktitle = {Proceedings 23. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:35:56GMT},
    date-modified = {2017-03-07 23:46:51 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {Bart16e, bartzPublic, free, Bart16n},
    organization = {Institut f{\"u}r Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut f{\"u}r Technologie},
    pages = {177--195},
    publisher = {KIT Scientific Publishing},
    rating = {0},
    title = {{Simulation and Optimization of Cyclone Dust Separators}},
    year = {2013},
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  • [PDF] [DOI] Oliver Flasch and Thomas Bartz-Beielstein. A Framework for the Empirical Analysis of Genetic Programming System Performance. In Rick Riolo, Ekaterina Vladislavleva, and Jason H. Moore, editors, Genetic programming theory and practice x, page 155–170. Springer, Ann Arbor, USA, 2013.
    [Bibtex]
    @incollection{Flas12c,
    abstract = {This chapter introduces a framework for statistical sound, reproducible empirical research in Genetic Programming (GP). It provides tools to understand GP algorithms and heuristics and their interaction with prob- lems of varying difficulty. Following an approach where scientific claims are broken down to testable statistical hypotheses and GP runs are treated as experiments, the framework helps to achieve statistically verified results of high reproducibility.},
    address = {Ann Arbor, USA},
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas},
    booktitle = {Genetic Programming Theory and Practice X},
    date-added = {2015-11-29T01:40:09GMT},
    date-modified = {2017-03-06 22:14:57 +0000},
    doi = {10.1007/978-1-4614-6846-2_11},
    editor = {Riolo, Rick and Vladislavleva, Ekaterina and Moore, Jason H},
    keywords = {bartzPublic, nonfree},
    pages = {155--170},
    publisher = {Springer},
    rating = {0},
    title = {{A Framework for the Empirical Analysis of Genetic Programming System Performance}},
    year = {2013},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/978-1-4614-6846-2_11}}
  • [PDF] [DOI] Oliver Flasch, Martina Friese, Katya Vladislavleva, Thomas Bartz-Beielstein, Olaf Mersmann, Boris Naujoks, Jörg Stork, and Martin Zaefferer. Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data. In AnnaI Esparcia-Alcázar, editor, Applications of evolutionary computation, page 172–181. Springer berlin heidelberg, Berlin, Heidelberg, 2013.
    [Bibtex]
    @incollection{Flas12d,
    abstract = {This work provides a preliminary study on applying state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart metering equipment. We compare a custom-build commercial baseline method to modern ensemble-based methods from statistical time-series analysis and to a modern commercial GP system. Our preliminary results indicate that that modern ensemble-based methods, as well as GP, are an attractive alternative to custom-built approaches for electrical energy consumption forecasting.
    },
    address = {Berlin, Heidelberg},
    author = {Flasch, Oliver and Friese, Martina and Vladislavleva, Katya and Bartz-Beielstein, Thomas and Mersmann, Olaf and Naujoks, Boris and Stork, J{\"o}rg and Zaefferer, Martin},
    booktitle = {Applications of Evolutionary Computation},
    date-added = {2016-08-19T14:05:35GMT},
    date-modified = {2017-03-07 09:09:12 +0000},
    doi = {10.1007/978-3-642-37192-9_18},
    editor = {Esparcia-Alc{\'a}zar, AnnaI},
    isbn = {978-3-642-37191-2},
    keywords = {bartzPublic, nonfree},
    pages = {172--181},
    publisher = {Springer Berlin Heidelberg},
    rating = {0},
    title = {{Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data}},
    url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84875669491&partnerID=MN8TOARS},
    year = {2013},
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    bdsk-url-3 = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84875669491&partnerID=MN8TOARS}}
  • [PDF] Martina Friese, Jörg Stork, Ricardo Ramos Guerra, Thomas Bartz-Beielstein, Soham Thaker, Oliver Flasch, and Martin Zaefferer. UniFIeD Univariate Frequency-based Imputation for Time Series Data. Technical Report, Betzdorfer Str. 2, 50679 Köln, 2013.
    [Bibtex]
    @techreport{Frie13acos,
    address = {Betzdorfer Str. 2, 50679 K{\"o}ln},
    author = {Friese, Martina and Stork, J{\"o}rg and Guerra, Ricardo Ramos and Bartz-Beielstein, Thomas and Thaker, Soham and Flasch, Oliver and Zaefferer, Martin},
    date-added = {2016-09-18T15:51:55GMT},
    date-modified = {2017-03-03 10:54:23 +0000},
    isbn = {2194-2870},
    keywords = {bartzPublic, free},
    language = {English},
    publisher = {Bibliothek der Fachhochschule K{\"o}ln},
    rating = {0},
    title = {{UniFIeD Univariate Frequency-based Imputation for Time Series Data}},
    url = {http://opus.bsz-bw.de/fhk/volltexte/2013/49},
    year = {2013},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://opus.bsz-bw.de/fhk/volltexte/2013/49}}
  • [PDF] Thomas Ludwig, Sandor Markon, Thomas Bartz-Beielstein, Michael Bongards, and Christoph Ament. Power optimization of linear motor elevators using computational intelligence methods. In JSPS summer program, page 44, Hayama, jun 2013. The graduate university for advanced studies (sokendai).
    [Bibtex]
    @inproceedings{Ludw13a,
    abstract = {This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace's equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications. Note: This course was previously called {\&}quot;Mathematical Methods for Engineers I.{\&}quot;},
    address = {Hayama},
    affiliation = {The Graduate University for Advanced Studies (Sokendai), Shonan Village, Hayama,Kanagawa (Japan)},
    author = {Ludwig, Thomas and Markon, Sandor and Bartz-Beielstein, Thomas and Bongards, Michael and Ament, Christoph},
    booktitle = {{JSPS} Summer Program},
    date-added = {2016-09-21T08:51:06GMT},
    date-modified = {2017-01-14 15:27:08 +0000},
    keywords = {bartzPublic, free},
    month = jun,
    pages = {44},
    publisher = {The Graduate University for Advanced Studies (SOKENDAI)},
    rating = {0},
    title = {Power Optimization of Linear Motor Elevators Using Computational Intelligence Methods},
    year = {2013},
    bdsk-file-1 = {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}}
  • [PDF] Martin Zaefferer, T. Bartz-Beielstein, B. Naujoks, T. Wagner, and M. Emmerich. A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets. In R. C. Purshouse and others, editors, Evolutionary multi-criterion optimization 7th international conference, emo, page 756–770, Heidelberg, 2013. Springer.
    [Bibtex]
    @inproceedings{Zaef13a,
    abstract = {Several methods were developed to solve cost-extensive multicriteria optimization problems by reducing the number of function evaluations by means of surrogate optimization. In this study, we apply different multi-criteria surrogate optimization methods to improve (tune) an event-detection software for water-quality monitoring. For tuning two important parameters of this software, four state-of-the-art methods are compared: S-Metric-Selection E cient Global Optimization (SMS- EGO), S-Metric-Expected Improvement for Efficient Global Optimization SExI-EGO, Euclidean Distance based Expected Improvement Euclid- EI (here referred to as MEI-SPOT due to its implementation in the Se- quential Parameter Optimization Toolbox SPOT) and a multi-criteria approach based on SPO (MSPOT).
    Analyzing the performance of the different methods provides insight into the working-mechanisms of cutting-edge multi-criteria solvers. As one of the approaches, namely MSPOT, does not consider the prediction variance of the surrogate model, it is of interest whether this can lead to premature convergence on the practical tuning problem. Furthermore, all four approaches will be compared to a simple SMS-EMOA to validate that the use of surrogate models is justified on this problem.},
    address = {Heidelberg},
    author = {Zaefferer, Martin and Bartz-Beielstein, T and Naujoks, B and Wagner, T and Emmerich, M},
    booktitle = {Evolutionary Multi-Criterion Optimization 7th International Conference, EMO},
    date-added = {2015-11-29T01:43:47GMT},
    date-modified = {2017-03-06 22:12:18 +0000},
    editor = {Purshouse, R C and others},
    groups = {bartzPublic},
    keywords = {Surrogate, bartzPublic, nonfree},
    pages = {756--770},
    publisher = {Springer},
    rating = {0},
    read = {Yes},
    timestamp = {2016-10-25},
    title = {{A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets}},
    year = {2013},
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  • [PDF] Martin Zaefferer, Boris Naujoks, and Thomas Bartz-Beielstein. A Gentle Introduction to Multi-Criteria Optimization with SPOT. CIplus Report 1/2013, TH Köln, 2013.
    [Bibtex]
    @techreport{Zaef13ccos,
    author = {Zaefferer, Martin and Naujoks, Boris and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:37:07GMT},
    date-modified = {2021-07-23 22:33:51 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    number = {1/2013},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{A Gentle Introduction to Multi-Criteria Optimization with SPOT}},
    type = {CIplus Report},
    year = {2013},
    bdsk-file-1 = {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}}

2012

  • [PDF] Thomas Bartz-Beielstein and Martin Zaefferer. A Gentle Introduction to Sequential Parameter Optimization. CIplus Report 1/2012, Fakultät 10 / Institut für Informatik, 2012. http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-191
    [Bibtex]
    @techreport{Bart12i,
    abstract = {There is a strong need for sound statistical analysis of simulation and optimization algorithms. Based on this analysis, improved parameter set- tings can be determined. This will be referred to as tuning. Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization toolbox SPOT package for R [5] is a toolbox for tuning and understanding simulation and optimization algorithms. The toolbox includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as classification and regressions trees (CART) and random forest; Gaussian process models (Kriging), and combinations of di erent meta-modeling approaches. This article exemplifies how an existing optimization algo- rithm, namely simulated annealing, can be tuned using the SPOT frame- work.},
    author = {Thomas Bartz-Beielstein and Martin Zaefferer},
    date-added = {2015-11-29T01:39:31GMT},
    date-modified = {2021-07-23 22:34:40 +0200},
    institution = {Fakult{\"a}t 10 / Institut f{\"u}r Informatik},
    keywords = {bartzPublic, free, frie17a},
    note = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-191},
    number = {1/2012},
    publisher = {CIplus},
    rating = {0},
    title = {{A Gentle Introduction to Sequential Parameter Optimization}},
    type = {CIplus Report},
    url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-191},
    year = {2012},
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    bdsk-url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-191}}
  • [PDF] Thomas Bartz-Beielstein. Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results. CIplus Report 3/2012, FH Köln, 11 2012.
    [Bibtex]
    @techreport{Bart12x,
    abstract = {Computational intelligence methods have gained importance in several real-world domains such as process optimization, system iden- tification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, sev- eral test suites were presented and considered as state of the art. How- ever, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world set- tings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on stan- dard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: prob- lem generation and statistical analysis of computer experiments.},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:37:33GMT},
    date-modified = {2021-07-23 22:35:51 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic},
    month = 11,
    number = {3/2012},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results}},
    type = {CIplus Report},
    year = {2012},
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  • [DOI] Thomas Bartz-Beielstein, Oliver Flasch, and Martin Zaefferer. Sequential parameter optimization for symbolic regression. In Proceedings of the 14th annual conference companion on genetic and evolutionary computation, GECCO ’12, page 495–496, New York, NY, USA, 2012. Acm.
    [Bibtex]
    @inproceedings{Bart12z,
    acmid = {2330861},
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, Thomas and Flasch, Oliver and Zaefferer, Martin},
    booktitle = {Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation},
    date-added = {2016-11-15 15:39:13 +0000},
    date-modified = {2016-11-15 15:42:42 +0000},
    doi = {10.1145/2330784.2330861},
    isbn = {978-1-4503-1178-6},
    keywords = {sequential parameter optimization, bartzPublic, Bart16n},
    location = {Philadelphia, Pennsylvania, USA},
    numpages = {2},
    pages = {495--496},
    publisher = {ACM},
    series = {GECCO '12},
    title = {Sequential Parameter Optimization for Symbolic Regression},
    url = {http://doi.acm.org/10.1145/2330784.2330861},
    year = {2012},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/2330784.2330861},
    bdsk-url-2 = {http://dx.doi.org/10.1145/2330784.2330861}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks, and Martin Zaefferer. SPOT Applied to Non-Stochastic Optimization Problems–-An Experimental Study. In Katya Rodriguez and Christian Blum, editors, Gecco 2012 late breaking abstracts workshop, page 645–646, Philadelphia, Pennsylvania, USA, jul 2012. Acm.
    [Bibtex]
    @inproceedings{Bart12d,
    abstract = {Most parameter tuning methods feature a number of pa- rameters themselves. This also holds for the Sequential Pa- rameter Optimization [1] Toolbox (SPOT1). It provides de- fault values, which are reasonable for many problems, but these defaults are set to favor robustness over performance.
    By default, a Random Forest (RF) [2] model is used for the surrogate optimization. The RF model is built rather fast. It runs robustly (i.e. it does not crash) and can handle non-ordered parameters (i.e. factors) very well. However, the RF model does provide poor optimization performance for a number of problems, due to the inbuilt discontinuities. It would often be more reasonable to use Kriging models [4]. These usually perform well for small and medium sized decision space dimensions. For use with the SPOT pack- age, there are several existing packages that provide Kriging methods that often fit the required problem well (DiceKrig- ing, mlegp, etc.). However, these methods have one thing in common, they are not robust. Especially when several de- sign points (samples in the decision space) are close to each other, those functions often fail. Hence, in SPOT versions greater 1.0, a Kriging model based on the Matlab code by Forrester et.al. [3] was introduced.},
    address = {Philadelphia, Pennsylvania, USA},
    annote = {Distributed at GECCO-2012. ACM Order Number 910122.},
    author = {Bartz-Beielstein, Thomas and Friese, Martina and Naujoks, Boris and Zaefferer, Martin},
    booktitle = {GECCO 2012 Late breaking abstracts workshop},
    date-added = {2015-11-29T01:38:41GMT},
    date-modified = {2017-03-08 22:09:49 +0000},
    doi = {doi:10.1145/2330784.2330901},
    editor = {Rodriguez, Katya and Blum, Christian},
    isbn = {978-1-4503-1178-6},
    keywords = {bartzPublic, BartzTutorial, nonfree},
    month = jul,
    pages = {645--646},
    publisher = {ACM},
    rating = {0},
    title = {{SPOT Applied to Non-Stochastic Optimization Problems---An Experimental Study}},
    year = {2012},
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    bdsk-url-1 = {http://dx.doi.org/10.1145/2330784.2330901}}
  • [PDF] Thomas Bartz-Beielstein. SpotSeven brochure 2012. 02 2012.
    [Bibtex]
    @booklet{Bart12j,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:28GMT},
    date-modified = {2021-07-23 22:35:25 +0200},
    keywords = {bartzPublic, free},
    month = 02,
    rating = {0},
    title = {{SpotSeven brochure 2012}},
    year = {2012},
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  • [PDF] [DOI] Thomas Bartz-Beielstein, Mike Preuss, and Martin Zaefferer. Statistical Analysis of Optimization Algorithms with R. In Gabriela Ochoa, editor, Gecco 2012 specialized techniques and applications tutorials, page 1259–1286, Philadelphia, Pennsylvania, USA, jul 2012. Acm.
    [Bibtex]
    @inproceedings{Bart12f,
    abstract = {Based on experiences from several (rather theoretical) tutorials and workshops devoted to the experimental analysis of algorithms at the world's leading conferences in the field of Computational Intelligence, a practical, hands-on tutorial for the statistical analysis of optimization algorithms is presented. This tutorial -demonstrates how to analyze results from real experimental studies, e.g., experimental studies in EC -item gives a comprehensive introduction in the R language -item introduces the powerful GUI rstudio (http://rstudio.org) -exemplifies the analysis using SPOT (http://cran.r-project.org/web/packages/SPOT/) R is the most attractive and fastest growing open source computer language for statistical computing and graphics in the world. It provides a wide variety of statistical and graphical techniques: linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, etc. R is distributed over CRAN (http://cran.r-project.org), which is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R.},
    address = {Philadelphia, Pennsylvania, USA},
    annote = {Also known as 2330940 Distributed at GECCO-2012. ACM Order Number 910122.},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Zaefferer, Martin},
    booktitle = {GECCO 2012 Specialized techniques and applications tutorials},
    date-added = {2015-11-29T01:39:02GMT},
    date-modified = {2017-01-14 14:57:22 +0000},
    doi = {10.1145/2330784.2330940},
    editor = {Ochoa, Gabriela},
    isbn = {978-1-4503-1178-6},
    keywords = {bartzPublic, Bart16n, BartzTutorial, nonfree},
    month = jul,
    pages = {1259--1286},
    publisher = {ACM},
    rating = {0},
    title = {{Statistical Analysis of Optimization Algorithms with R}},
    year = {2012},
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    bdsk-url-1 = {http://dx.doi.org/10.1145/2330784.2330940}}
  • [PDF] Oliver Flasch and Thomas Bartz-Beielstein. Towards a Framework for the Empirical Analysis of Genetic Programming System Performance. Technical Report, Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany, may 2012.
    [Bibtex]
    @techreport{Flas12a,
    abstract = {This chapter introduces the basics of a framework for sta- tistical sound, reproducible empirical research in Genetic Programming (GP). It provides tools to understand GP algorithms and heuristics and their interaction with problems of varying di culty. Following a rigorous approach where scientific claims are broken down to testable statistical hypotheses and GP runs are treated as experiments, the framework helps to achieve statistically verified results of high reproducibility. A proto- typic software-implementation based on the R environment automates experiment setup, execution, and analysis. The framework is introduced by means of an example study comparing the performance of a refer- ence GP system (TinyGP) with a successively more complex variants of a more modern system (GMOGP) to test the intuition that complex problems require complex algorithms.},
    address = {Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany},
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:40:12GMT},
    date-modified = {2017-03-08 23:14:54 +0000},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = may,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Towards a Framework for the Empirical Analysis of Genetic Programming System Performance}},
    url = {http://maanvs03.gm.fh-koeln.de/webpub/CIOPReports.d/Flas12a.d/ciop0512.pdf},
    year = {2012},
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    bdsk-url-1 = {http://maanvs03.gm.fh-koeln.de/webpub/CIOPReports.d/Flas12a.d/ciop0512.pdf}}
  • [PDF] Martina Friese, Thomas Bartz-Beielstein, Katya Vladislavleva, Oliver Flasch, Olaf Mersmann, Boris Naujoks, Martin Zaefferer, and Jörg Stork. Ensemble-Based Model Selection for Smart Metering Data. In Frank Hoffmann and Eyke Hüllermeier, editors, Proceedings 22. workshop computational intelligence, page 215–228. Institut für Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut für Technologie, Kit scientific publishing, 2012.
    [Bibtex]
    @inproceedings{Frie12c,
    abstract = {In times of accelerating climate change and rising energy costs, increasing energy efficiency becomes a high-priority goal for business and private households alike. Smart metering equipment records energy consumption data in regular intervals multiple times per hour, streaming this data to a central system, usually located at a local public utility company. Here, consumption data can be correlated and analyzed to detect anomalies such as unusual high consumption.
    },
    affiliation = {Institut f{\"u}r Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut f{\"u}r Technologie},
    author = {Friese, Martina and Bartz-Beielstein, Thomas and Vladislavleva, Katya and Flasch, Oliver and Mersmann, Olaf and Naujoks, Boris and Zaefferer, Martin and Stork, J{\"o}rg},
    booktitle = {Proceedings 22. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:40:21GMT},
    date-modified = {2017-03-07 09:28:47 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {bartzPublic, free},
    organization = {Institut f{\"u}r Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut f{\"u}r Technologie},
    pages = {215--228},
    publisher = {KIT Scientific Publishing},
    rating = {0},
    title = {{Ensemble-Based Model Selection for Smart Metering Data}},
    year = {2012},
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  • [PDF] Martina Friese, Thomas Bartz-Beielstein, Katya Vladislavleva, Oliver Flasch, Olaf Mersmann, Boris Naujoks, Jörg Stork, and Martin Zaefferer. Ensemble-Based Model Selection for Smart Metering Data (Abstract). Technical Report, 2012.
    [Bibtex]
    @techreport{Frie12a,
    author = {Friese, Martina and Bartz-Beielstein, Thomas and Vladislavleva, Katya and Flasch, Oliver and Mersmann, Olaf and Naujoks, Boris and Stork, J{\"o}rg and Zaefferer, Martin},
    date-added = {2015-11-29T01:40:21GMT},
    date-modified = {2017-01-14 15:23:00 +0000},
    keywords = {bartzPublic, free},
    publisher = {{CI}plus},
    rating = {0},
    title = {{Ensemble-Based Model Selection for Smart Metering Data (Abstract)}},
    year = {2012},
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  • [PDF] [DOI] Patrick Koch, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, Claus Weihs, and Wolfgang Konen. Tuning and evolution of support vector kernels. Evolutionary intelligence, 5(3):153–170, 2012.
    [Bibtex]
    @article{Koch12b,
    abstract = {Kernel-based methods like Support Vector Machines (SVM) have been established as powerful techniques in machine learning. The idea of SVM is to perform a mapping from the input space to a higher-dimensional feature space using a kernel function, so that a linear learning algorithm can be employed. However, the burden of choosing the appropriate kernel function is usually left to the user. It can easily be shown that the accuracy of the learned model highly depends on the chosen kernel function and its parameters, especially for complex tasks. In order to obtain a good classification or regression model, an appropriate kernel function in combination with optimized pre- and post-processed data must be used. To circumvent these obstacles, we present two solutions for optimizing kernel functions: (a) automated hyperparameter tuning of kernel functions combined with an optimization of pre- and post-processing options by Sequential Parameter Optimization (SPO) and (b) evolving new kernel functions by Genetic Programming (GP). We review modern techniques for both approaches, comparing their different strengths and weaknesses. We apply tuning to SVM kernels for both regression and classification. Automatic hyperparameter tuning of standard kernels and pre- and post-processing options always yielded to systems with excellent prediction accuracy on the considered problems. Especially SPO-tuned kernels lead to much better results than all other tested tuning approaches. Regarding GP-based kernel evolution, our method rediscovered multiple standard kernels, but no significant improvements over standard kernels were obtained.},
    author = {Koch, Patrick and Bischl, Bernd and Flasch, Oliver and Bartz-Beielstein, Thomas and Weihs, Claus and Konen, Wolfgang},
    date-added = {2015-11-29T01:37:34GMT},
    date-modified = {2017-03-08 23:20:32 +0000},
    doi = {10.1007/s12065-012-0073-8},
    journal = {Evolutionary Intelligence},
    keywords = {bartzPublic, nonfree},
    language = {English},
    number = {3},
    pages = {153--170},
    publisher = {Springer-Verlag},
    rating = {0},
    title = {{Tuning and evolution of support vector kernels}},
    volume = {5},
    year = {2012},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/s12065-012-0073-8}}
  • [DOI] Gabriela Ochoa, Mike Preuss, Thomas Bartz-Beielstein, and Marc Schoenauer. Editorial for the special issue on automated design and assessment of heuristic search methods. Evolutionary computation, 20(2):161–163, 12 2012.
    [Bibtex]
    @article{Ocho12a,
    annote = {doi: 10.1162/EVCO{\_}e{\_}00071},
    author = {Ochoa, Gabriela and Preuss, Mike and Bartz-Beielstein, Thomas and Schoenauer, Marc},
    booktitle = {Evolutionary Computation},
    date = {2012/05/07},
    date-added = {2018-12-02 18:13:58 +0100},
    date-modified = {2020-05-26 19:41:14 +0200},
    doi = {10.1162/EVCO{\_}e{\_}00071},
    isbn = {1063-6560},
    journal = {Evolutionary Computation},
    journal1 = {Evolutionary Computation},
    keywords = {bartzPublic, nonfree},
    m3 = {doi: 10.1162/EVCO{\_}e{\_}00071},
    month = {12},
    number = {2},
    pages = {161--163},
    publisher = {MIT Press},
    title = {Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods},
    ty = {JOUR},
    url = {https://doi.org/10.1162/EVCO_e_00071},
    volume = {20},
    year = {2012},
    year1 = {2012},
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  • [PDF] Martin Zaefferer, B. Naujoks, T. Bartz-Beielstein, M. Friese, O. Mersmann, and Oliver Flasch. Mehrkriterielle sequentielle Parameteroptimierung für Anwendungsprobleme mit stark limitiertem Budget. In Frank Hoffmann and Eyke Hüllermeier, editors, Proceedings 22. workshop computational intelligence, page 385–400. Institut für Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut für Technologie, Kit scientific publishing, 2012.
    [Bibtex]
    @inproceedings{Zaef12c,
    abstract = {Sequentielle Parameteroptimierung (SPO, [1]) wurde bisher haupts{\"a}chlich f{\"u}r einkriterielle Anwendungen eingesetzt. Die sequentielle Optimierung mit Surrogatmodellen eignet sich, um bei kosten- und zeitintensiven Problemen reale Funktionsauswertungen einzusparen. In der Praxis ergeben sich h{\"a}ufig Problemstellungen, die nur mit wenigen Zielfunktionsauswer- tungen (einem geringen Budget) zu l{\"o}sen sind und zus{\"a}tzlich mehrere Zielkriterien aufweisen. F{\"u}r diese Anwendungen wurde die SPO Toolbox SPOT f{\"u}r die mehrkriterielle Optimierung erweitert. Beispiele f{\"u}r diese Anwendungsf{\"a}lle sind die Optimierung von Staubabscheidern in Kohle- kraftwerken oder die Optimierung von Fehlalarmraten und Erkennungs- raten zur Detektion von Anomalien in Trinkwasserdaten. Die vorliegende Fallstudie befasst sich mit bekannten Testfunktionen, um die Anwendbar- keit verschiedener Ans{\"a}tze zu pr{\"u}fen.
    },
    affiliation = {Institut f{\"u}r Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut f{\"u}r Technologie},
    author = {Zaefferer, Martin and Naujoks, B and Bartz-Beielstein, T and Friese, M and Mersmann, O and Flasch, Oliver},
    booktitle = {Proceedings 22. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:43:43GMT},
    date-modified = {2017-03-07 22:04:38 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {bartzPublic, free},
    organization = {Institut f{\"u}r Angewandte Informatik/Automatisierungstechnik am Karlsruher Institut f{\"u}r Technologie},
    pages = {385--400},
    publisher = {KIT Scientific Publishing},
    rating = {0},
    title = {{Mehrkriterielle sequentielle Parameteroptimierung f{\"u}r Anwendungsprobleme mit stark limitiertem Budget}},
    year = {2012},
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  • [PDF] Martin Zaefferer, Thomas Bartz-Beielstein, Boris Naujoks, Tobias Wagner, and Michael Emmeric. Model-assisted Multi-criteria Tuning of an Event Detection Software under Limited Budgets. CIplus Report 2/2012, TH Köln, 10 2012.
    [Bibtex]
    @techreport{Zaef12ecos,
    abstract = {Formerly, multi-criteria optimization algorithms were often tested using tens of thousands function evaluations. In many real-world settings function evaluations are very costly or the available budget is very limited. Several methods were developed to solve these cost- extensive multi-criteria optimization problems by reducing the number of function evaluations by means of surrogate optimization. In this study, we apply different multi-criteria surrogate optimization methods to im- prove (tune) an event-detection software for water-quality monitoring. For tuning two important parameters of this software, four state-of-the- art methods are compared: S-Metric-Selection Efficient Global Optimization (SMS-EGO), S-Metric-Expected Improvement for Efficient Global Optimization SExI-EGO, Euclidean Distance based Expected Improve- ment Euclid-EI (here referred to as MEI-SPOT due to its implementa- tion in the Sequential Parameter Optimization Toolbox SPOT) and a multi-criteria approach based on SPO (MSPOT).
    Analyzing the performance of the different methods provides insight into the working-mechanisms of cutting-edge multi-criteria solvers. As one of the approaches, namely MSPOT, does not consider the prediction variance of the surrogate model, it is of interest whether this can lead to premature convergence on the practical tuning problem. Furthermore, all four approaches will be compared to a simple SMS-EMOA to validate that the use of surrogate models is justified on this problem.},
    author = {Zaefferer, Martin and Bartz-Beielstein, Thomas and Naujoks, Boris and Wagner, Tobias and Emmeric, Michael},
    date-added = {2015-11-29T01:43:46GMT},
    date-modified = {2021-07-23 22:37:06 +0200},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    local-url = {file://localhost/Users/bartz/Library/Mobile%20Documents/com~apple~CloudDocs/Papers3.d/Papers%20Library/Files/65/65A6E01E-B901-49AA-9490-BFCCAFE525A8.pdf},
    month = 10,
    number = {2/2012},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Model-assisted Multi-criteria Tuning of an Event Detection Software under Limited Budgets}},
    type = {CIplus Report},
    year = {2012},
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  • [PDF] Martin Zaefferer, Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks, and Oliver Flasch. MSPOT: Multi-Criteria Sequential Parameter Optimization. Technical Report TR 2/2012, TH Köln, jan 2012.
    [Bibtex]
    @techreport{Zaef12b,
    abstract = {Many relevant industrial optimization tasks feature more than just one quality criterion. State-of-the-art multi-criteria optimization algorithms require a relatively large number of function evaluations (usually more than 105) to approximate Pareto fronts. Due to high cost or time con- sumption this large amount of function evaluations is not always available. Therefore, it is obvious to combine techniques such as Sequential Param- eter Optimization (SPO), which need a very small number of function evaluations only, with techniques from evolutionary multi-criteria opti- mization (EMO). In this paper, we show how EMO techniques can be e ciently integrated into the framework of the SPO Toolbox (SPOT). We discuss advantages of this approach in comparison to state-of-the-art optimizers. Moreover, with the resulting capability to allow competing objectives, the opportunity arises to not only aim for the best, but also for the most robust solution. Herein we present an approach to optimize not only the quality of the solution, but also its robustness, taking these two goals as objectives for multi-criteria optimization into account.},
    author = {Martin Zaefferer and Thomas Bartz-Beielstein and Martina Friese and Boris Naujoks and Oliver Flasch},
    date-added = {2015-11-29T01:43:42GMT},
    date-modified = {2017-03-07 22:34:02 +0000},
    institution = {TH K{\"o}ln},
    keywords = {Bart16e, bartzPublic, free, Bart16n},
    month = jan,
    number = {TR 2/2012},
    publisher = {{CI}plus},
    rating = {0},
    title = {{MSPOT: Multi-Criteria Sequential Parameter Optimization}},
    year = {2012},
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  • [PDF] [DOI] Martin Zaefferer, Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks, and Oliver Flasch. Multi-Criteria Optimization for Hard Problems under Limited Budgets. In Terry Soule and others, editors, Gecco companion ’12: proceedings of the fourteenth international conference on genetic and evolutionary computation conference companion, page 1451–1452, Philadelphia, Pennsylvania, USA, jul 2012. Acm.
    [Bibtex]
    @inproceedings{Zaef12a,
    abstract = {Many relevant industrial optimization tasks feature more than just one quality criterion. State-of-the-art multi-criteria optimization algorithms require a relatively large number of function evaluations (usually more than 10^5) to approximate Pareto fronts. Due to high cost or time consumption this large amount of function evaluations is not always available. Therefore, it is obvious to combine techniques such as Sequential Parameter Optimization (SPO), which need a very small number of function evaluations only, with techniques from evolutionary multi-criteria optimization (EMO). In this paper, we show how EMO techniques can be efficiently integrated into the framework of the SPO Toolbox (SPOT). We discuss advantages of this approach in comparison to state-of-the-art optimizers. Moreover, with the resulting capability to allow competing objectives, the opportunity arises to not only aim for the best, but also for the most robust solution. Herein we present an approach to optimize not only the quality of the solution, but also its robustness, taking these two goals as objectives for multi-criteria optimization into account.},
    address = {Philadelphia, Pennsylvania, USA},
    annote = {Distributed at GECCO-2012. ACM Order Number 910122.},
    author = {Zaefferer, Martin and Bartz-Beielstein, Thomas and Friese, Martina and Naujoks, Boris and Flasch, Oliver},
    booktitle = {GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion},
    date-added = {2015-11-29T01:43:45GMT},
    date-modified = {2017-01-14 15:31:08 +0000},
    doi = {doi:10.1145/2330784.2330984},
    editor = {Soule, Terry and others},
    isbn = {978-1-4503-1178-6},
    keywords = {Bart16e, bartzPublic, nonfree},
    month = jul,
    pages = {1451--1452},
    publisher = {ACM},
    rating = {0},
    title = {{Multi-Criteria Optimization for Hard Problems under Limited Budgets}},
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    bdsk-url-1 = {http://dx.doi.org/10.1145/2330784.2330984}}

2011

  • [DOI] Thomas Bartz-Beielstein and Mike Preuss. Automatic and interactive tuning of algorithms. In Proceedings of the 13th annual conference companion on genetic and evolutionary computation, GECCO ’11, page 1361–1380, New York, NY, USA, 2011. Association for computing machinery.
    [Bibtex]
    @inproceedings{Bart11g,
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation},
    date-added = {2020-07-08 16:45:25 +0200},
    date-modified = {2020-07-08 16:46:37 +0200},
    doi = {10.1145/2001858.2002141},
    isbn = {9781450306904},
    keywords = {evolutionary algorithms, tuning, kriging, experimental methodology, bartzPublic, BartzTutorial, nonfree},
    location = {Dublin, Ireland},
    numpages = {20},
    pages = {1361--1380},
    publisher = {Association for Computing Machinery},
    series = {GECCO '11},
    title = {Automatic and Interactive Tuning of Algorithms},
    url = {https://doi.org/10.1145/2001858.2002141},
    year = {2011},
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    bdsk-url-1 = {https://doi.org/10.1145/2001858.2002141}}
  • [PDF] Thomas Bartz-Beielstein, Martina Friese, Oliver Flasch, Wolfgang Konen, Patrick Koch, and Boris Naujoks. Ensemble-Based Modeling. Working paper, FH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 06 2011.
    [Bibtex]
    @techreport{Bart11e,
    abstract = {Sequential parameter optimization (SPO) can be described as a tuning algorithm with the following properties[Bartz-Beielstein et al., 2004]: (i) Use the available budget (e.g., simulator runs, number of function evaluations) sequentially, i.e., use information from search-space exploration to guide the search by building one or several meta models, e.g., random forest, linear regression, or Kriging. Choose new design points based on predictions from the meta model(s). Refine the meta model(s) stepwise to improve knowledge about the search space. (ii) Try to cope with noise by improving confidence. Guarantee comparable confidence for search points. (iii) Collect and report tuning process information for exploratory data analysis. (iv) Provide mechanisms both for interactive and automated tuning.
    The SPO toolbox (SPOT) provides standardized interfaces, which enable the integration of several meta models in a convenient manner [Bartz-Beielstein et al., 2010].1 Naturally, the question arises, which meta model should be used during the tuning process. Instead of recommending one meta model only, we will analyze an alternative approach: Set up several models in parallel, and provide an effective and efficient policy for dynamical model selection.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas and Friese, Martina and Flasch, Oliver and Konen, Wolfgang and Koch, Patrick and Naujoks, Boris},
    date-added = {2015-11-29T01:38:39GMT},
    date-modified = {2021-07-23 22:38:35 +0200},
    institution = {FH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = 06,
    rating = {0},
    title = {{Ensemble-Based Modeling}},
    type = {Working paper},
    year = {2011},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Martina Friese, Martin Zaefferer, Boris Naujoks, Oliver Flasch, Wolfgang Konen, and Patrick Koch. Noisy optimization with sequential parameter optimization and optimal computational budget allocation. In Proceedings of the 13th annual conference companion on genetic and evolutionary computation, page 119–120, New York, NY, USA, 2011. Acm.
    [Bibtex]
    @inproceedings{Bart11b,
    abstract = {Sequential parameter optimization (SPO) is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. It includes a broad variety of meta models, e.g., linear models, random forest, and Gaussian process models (Kriging). The selection of an adequate meta model can have significant impact on SPO's performance. A comparison of different meta models is of great importance. A recent study indicated that random forest based meta models might be a good choice. This rather surprising result will be analyzed in this paper.
    Moreover, Optimal Computing Budget Allocation (OCBA), which is an en- hanced method for handling the computational budget spent for selecting new de- sign points, is presented. The OCBA approach can intelligently determine the most efficient replication numbers. We propose the integration of OCBA into SPO.
    In this study, SPO is directly used as an optimization method on different noisy mathematical test functions. This is differs from the standard way of using SPO for tuning algorithm parameters in the context of complex real-world applications. Using SPO this way allows for a comparison to other optimization algorithms.
    Our results reveal that the incorporation of OCBA and the selection of Gaus- sian process models are highly beneficial. Moreover, SPO outperformed three different alternative optimization algorithms on a set of five noisy mathematical test functions.},
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, Thomas and Friese, Martina and Zaefferer, Martin and Naujoks, Boris and Flasch, Oliver and Konen, Wolfgang and Koch, Patrick},
    booktitle = {Proceedings of the 13th annual conference companion on Genetic and evolutionary computation},
    date-added = {2015-11-29T01:38:43GMT},
    date-modified = {2017-03-07 22:46:00 +0000},
    doi = {10.1145/2001858.2001926},
    isbn = {978-1-4503-0690-4},
    keywords = {bartzPublic, nonfree},
    pages = {119--120},
    publisher = {ACM},
    rating = {0},
    title = {{Noisy optimization with sequential parameter optimization and optimal computational budget allocation}},
    year = {2011},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/2001858.2001926},
    bdsk-url-2 = {http://dx.doi.org/10.1145/2001858.2001926}}
  • [PDF] Thomas Bartz-Beielstein and Martina Friese. Sequential Parameter Optimization and Optimal Computational Budget Allocation for Noisy Optimization Problems. Technical Report, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, jan 2011.
    [Bibtex]
    @techreport{Bart11a,
    abstract = {Sequential parameter optimization (SPO) is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. It includes a broad vari- ety of meta models, e.g., linear models, random forest, and Gaussian process models (Kriging). The selection of an adequate meta model can have significant impact on SPO's performance. A comparison of different meta models is of great importance. A recent study indicated that random forest based meta models might be a good choice. This rather surprising result will be analyzed in this paper.
    Moreover, Optimal Computing Budget Allocation (OCBA), which is an enhanced method for handling the computational budget spent for selecting new design points, is presented. The OCBA approach can intelligently determine the most efficient replication numbers. We propose the integration of OCBA into SPO.
    In this study, SPO is directly used as an optimization method on different noisy mathemat- ical test functions. This is differs from the standard way of using SPO for tuning algorithm parameters in the context of complex real-world applications. Using SPO this way allows for a comparison to other optimization algorithms.
    Our results reveal that the incorporation of OCBA and the selection of Gaussian pro- cess models are highly beneficial. Moreover, SPO outperformed three different alternative optimization algorithms on a set of five noisy mathematical test functions.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas and Friese, Martina},
    date-added = {2015-11-29T01:38:42GMT},
    date-modified = {2017-03-07 23:39:06 +0000},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = jan,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization andData Mining)},
    rating = {0},
    title = {{Sequential Parameter Optimization and Optimal Computational Budget Allocation for Noisy Optimization Problems}},
    year = {2011},
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  • [PDF] Thomas Bartz-Beielstein and Martin Zaefferer. SPOT Package Vignette. Technical Report, FH Köln, 2011.
    [Bibtex]
    @techreport{Bart11m,
    abstract = {The main goal of this vignette is to demonstrate the usage of SPOT Package functions and their interfaces. For more detailed information on how to work with spot scientificly, check the literature, as this is basically just a technical description on how to use the different functions. A more detailed manual that explains how to use these functions successfully can be found in the overview of user guides and package vignettes (see help index).},
    author = {Bartz-Beielstein, Thomas and Zaefferer, Martin},
    date-added = {2015-11-29T01:39:31GMT},
    date-modified = {2021-07-23 22:38:56 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free, frie17a},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{SPOT Package Vignette}},
    url = {http://cran.r-project.org/web/packages/SPOT/vignettes/SPOT.pdf},
    year = {2011},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://cran.r-project.org/web/packages/SPOT/vignettes/SPOT.pdf}}
  • [PDF] Oliver Flasch, Thomas Bartz-Beielstein, Daniel Bicker 1, Wolfgang Kantschik, and Christian von Strachwitz. Results of the GECCO 2011 Industrial Challenge: Optimizing Foreign Exchange Trading Strategies. Technical Report, FH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 12 2011.
    [Bibtex]
    @techreport{Flas11a,
    abstract = {The GECCO 2011 Industrial Challenge posed a difficult real-world problem in financial time series forecasting, provided by Quaesta Capital GmbH. This report gives a problem overview, outlines the rules of the challenge and provides a result summary of the winning submission.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas and 1, Daniel Bicker and Kantschik, Wolfgang and von Strachwitz, Christian},
    date-added = {2015-11-29T01:40:10GMT},
    date-modified = {2021-07-24 10:23:12 +0200},
    institution = {FH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = 12,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Results of the GECCO 2011 Industrial Challenge: Optimizing Foreign Exchange Trading Strategies}},
    year = {2011},
    bdsk-file-1 = {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}}
  • [PDF] Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, Oliver Flasch, Patrick Koch, Wolfgang Konen, and Boris Naujoks. Ensemble-Based Optimization and Tuning Algorithms. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 21. workshop computational intelligence, page 119–134. Universitätsverlag karlsruhe, 2011.
    [Bibtex]
    @inproceedings{Frie11a,
    abstract = {Sequential parameter optimization (SPO)[1] is a state-of-the-art tuning methodology for optimization algorithms. It has the following properties:
    i) Use the available budget (e.g., simulator runs, number of function evaluations) se- quentially, i.e., use information from search-space exploration to guide the search by building one or several meta models, e.g., random forest, linear regression, or Kriging. Choose new design points based on predictions from the meta model(s). Refine the meta model(s) stepwise to improve knowledge about the search space.
    ii) Try to cope with noise by improving confidence. Guarantee comparable confidence for search points.
    iii) Collect and report tuning process information for exploratory data analysis.
    iv) Provide mechanisms both for interactive and automated tuning.},
    author = {Friese, Martina and Zaefferer, Martin and Bartz-Beielstein, Thomas and Flasch, Oliver and Koch, Patrick and Konen, Wolfgang and Naujoks, Boris},
    booktitle = {Proceedings 21. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:40:26GMT},
    date-modified = {2017-03-07 20:55:47 +0000},
    editor = {Hoffmann, F and H{\"u}llermeier, E},
    keywords = {bartzPublic, free},
    pages = {119--134},
    publisher = {Universit{\"a}tsverlag Karlsruhe},
    rating = {0},
    title = {{Ensemble-Based Optimization and Tuning Algorithms}},
    year = {2011},
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  • [PDF] Patrick Koch, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, and Wolfgang Konen. On the Tuning and Evolution of Support Vector Kernels. Technical Report, FH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 03 2011.
    [Bibtex]
    @techreport{Koch11a,
    abstract = {Kernel-based methods like Support Vector Machines (SVM) have been estab- lished as powerful techniques in machine learning. The idea of SVM is to perform a mapping from the input space to a higher-dimensional feature space using a kernel function k, so that a linear learning algorithm can be employed. However, the burden of choosing the appropriate kernel function is usually left to the user. It can easily be shown, that the accuracy of the learned model highly depends on the chosen kernel function and its parameters, especially for complex tasks. In order to obtain a good classification or regression model, an appropriate kernel function must be used.
    Design and hand-tuning of kernel functions can be time-consuming and requires expert knowledge. To circumvent these obstacles for the 'non-expert' data mining user, which may hinder the wider use of SVM in data mining, we present two solutions for optimizing kernel functions: (a) automated hyperparameter tuning of kernel functions and (b) evolving new kernel functions by Genetic Program- ming (GP). We review state-of-the-art techniques for both approaches, comparing their different strengths and weaknesses. Special attention is drawn to Sequential Parameter Optimization (SPO) for tuning, as this method also allows a statistical evaluation and understanding of the respective influences of the parameters.
    A tuned kernel can improve the trained model, if standard kernels are insufficient for achieving a good transformation. We apply tuning to SVM kernels for both regression and classification. In fact often a good kernel function is missing for real-world problems. We compare standard kernels with hand-tuned parameters to SPO-tuned standard kernels and to GP-generated custom kernels for these problems.
    },
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Koch, Patrick and Bischl, Bernd and Flasch, Oliver and Bartz-Beielstein, Thomas and Konen, Wolfgang},
    date-added = {2015-11-29T01:40:44GMT},
    date-modified = {2021-07-23 22:40:13 +0200},
    institution = {FH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = 03,
    rating = {0},
    title = {{On the Tuning and Evolution of Support Vector Kernels}},
    year = {2011},
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  • [PDF] Patrick Koch, Wolfgang Konen, Boris Naujoks, Oliver Flasch, Martina Friese, Martin Zaefferer, and Thomas Bartz-Beielstein. Tuned Data Mining in R. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 21. workshop computational intelligence, page 147–160. Universitätsverlag karlsruhe, 2011.
    [Bibtex]
    @inproceedings{Koch11b,
    author = {Koch, Patrick and Konen, Wolfgang and Naujoks, Boris and Flasch, Oliver and Friese, Martina and Zaefferer, Martin and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings 21. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:41:06GMT},
    date-modified = {2017-01-14 15:25:18 +0000},
    editor = {Hoffmann, F and H{\"u}llermeier, E},
    keywords = {bartzPublic, free},
    pages = {147--160},
    publisher = {Universit{\"a}tsverlag Karlsruhe},
    rating = {0},
    title = {{Tuned Data Mining in R}},
    uri = {\url{papers3://publication/uuid/DD94E1A0-65EA-4238-B778-F8751BFA0675}},
    year = {2011},
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  • [PDF] Wolfgang Konen, Patrick Koch, Oliver Flasch, Thomas Bartz-Beielstein, Martina Friese, and Boris Naujoks. Preprint: Tuned Data Mining: A Benchmark Study on Different Tuners. Technical Report, TH Köln, feb 2011.
    [Bibtex]
    @techreport{Kone11b,
    abstract = {In data mining (DM), the user has to deal with a variety of options to be set manually, e.g., which preprocessing is reasonable for the task, which models should be incorporated, what is an adequate parameter setting for models and preprocessing, or which variables should be selected. Such questions have to be answered anew for each task and illustrate the importance of a comprehensive framework, which can generate good models for DM tasks. We present the software package Tuned Data Mining in R (TDMR), which is a comprehensive framework for data mining offering a fully integrated concept for parameter tuning.
    },
    author = {Konen, Wolfgang and Koch, Patrick and Flasch, Oliver and Bartz-Beielstein, Thomas and Friese, Martina and Naujoks, Boris},
    date-added = {2015-11-29T01:41:08GMT},
    date-modified = {2017-03-07 23:26:38 +0000},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    month = feb,
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Preprint: Tuned Data Mining: A Benchmark Study on Different Tuners}},
    year = {2011},
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  • [PDF] [DOI] Wolfgang Konen, Patrick Koch, Oliver Flasch, Thomas Bartz-Beielstein, Martina Friese, and Boris Naujoks. Tuned Data Mining: A Benchmark Study on Different Tuners. In Natalio Krasnogor, editor, Gecco ’11: proceedings of the 13th annual conference on genetic and evolutionary computation, page 1995–2002, 2011.
    [Bibtex]
    @inproceedings{Kone11d,
    author = {Konen, Wolfgang and Koch, Patrick and Flasch, Oliver and Bartz-Beielstein, Thomas and Friese, Martina and Naujoks, Boris},
    booktitle = {GECCO '11: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation},
    date-added = {2015-11-29T01:41:04GMT},
    date-modified = {2017-03-08 23:15:46 +0000},
    doi = {10.1145/2001576.2001844},
    editor = {Krasnogor, Natalio},
    keywords = {bartzPublic, nonfree},
    pages = {1995--2002},
    rating = {0},
    title = {{Tuned Data Mining: A Benchmark Study on Different Tuners}},
    year = {2011},
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    bdsk-url-1 = {http://dx.doi.org/10.1145/2001576.2001844}}

2010

  • [DOI] Thomas Bartz-Beielstein, Marco Chiarandini, Luis Paquete, and Mike Preuss, editors. Experimental Methods for the Analysis of Optimization Algorithms. Springer, Berlin, Heidelberg, New York, 2010.
    [Bibtex]
    @book{Bart09a,
    abstract = {In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods.
    This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies.
    This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment of optimization algorithms and, consequently, their design.},
    address = {Berlin, Heidelberg, New York},
    date-added = {2015-11-29T01:43:52GMT},
    date-modified = {2017-03-07 21:22:52 +0000},
    doi = {10.1007/978-3-642-02538-9},
    editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    isbn = {978-3-642-02537-2},
    keywords = {bartzPublic, nonfree},
    publisher = {Springer},
    rating = {0},
    title = {{Experimental Methods for the Analysis of Optimization Algorithms}},
    url = {http://www.springer.com/978-3-642-02537-2},
    year = {2010},
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    bdsk-url-1 = {http://www.springer.com/978-3-642-02537-2},
    bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-02538-9}}
  • [PDF] T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors. Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010, 2010.
    [Bibtex]
    @proceedings{Bart10h,
    booktitle = {Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010},
    date-added = {2015-11-29T01:45:12GMT},
    date-modified = {2017-01-14 14:50:48 +0000},
    editor = {Bartz-Beielstein, T and Chiarandini, M and Paquete, L and Preuss, M},
    keywords = {bartzPublic, free},
    rating = {0},
    title = {{Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010}},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein, Mike Preuss, Karlheinz Schmitt, and Hans–Paul Schwefel. Challenges for Contemporary Evolutionary Algorithms. Technical Report, TU Dortmund, Faculty of Computer Science, Algorithm Engineering (Ls11), TU Dortmund, may 2010.
    [Bibtex]
    @techreport{Bart10j,
    abstract = {Does one need more than one optimization method? Or, stated differently, is there an optimal optimization method? Following from the No Free Lunch theo- rem (NFL, Wolpert and Macready [1]), in the general case---without clearly speci- fied task---there is not. For every single task, creating a specialized method would be advantageous. Unfortunately, this requires (i) a lot of effort, and (ii) extensive knowledge about the treated problem, and is thus not practiced. Alternatively, two strategies are usually followed when tackling a `new' optimization problem:
    -- Adapt an existing algorithm to the problem in its current form, and/or -- model/formulate the problem appropriately for an existing algorithm.},
    address = {Faculty of Computer Science, Algorithm Engineering (Ls11), TU Dortmund},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Schmitt, Karlheinz and Schwefel, Hans--Paul},
    date-added = {2015-11-29T01:39:08GMT},
    date-modified = {2021-07-24 10:21:47 +0200},
    institution = {TU Dortmund},
    keywords = {bartzPublic, free},
    month = may,
    rating = {0},
    title = {{Challenges for Contemporary Evolutionary Algorithms}},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein. Performing Meta Experiments Using the Sequential Parameter Optimization Toolbox SPOT. Technical Report, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2010.
    [Bibtex]
    @techreport{Bart10o,
    abstract = {The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta- modeling approaches. The goal of classical tuning is the determination of one good algorithm parameter setting for one specific problem instance. Using SPOT's meta mode, good parameter settings of one algorithm for several problem instances can be determined. This article exemplifies how meta experiments can be performed using the spot framework.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:32GMT},
    date-modified = {2018-11-16 20:11:11 +0100},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = aug,
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Performing Meta Experiments Using the Sequential Parameter Optimization Toolbox SPOT}},
    year = {2010},
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  • [DOI] Thomas Bartz-Beielstein and Mike Preuss. Tuning and experimental analysis in evolutionary computation: what we still have wrong. In Martin Pelikan and Jürgen Branke, editors, Gecco (companion), page 2625–2646, New York, New York, USA, 2010. Acm.
    [Bibtex]
    @inproceedings{Bart10i,
    address = {New York, New York, USA},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {GECCO (Companion)},
    date-added = {2015-11-29T01:39:07GMT},
    date-modified = {2017-01-14 15:56:55 +0000},
    doi = {10.1145/1830761.1830911},
    editor = {Pelikan, Martin and Branke, J{\"u}rgen},
    isbn = {978-1-4503-0073-5},
    keywords = {bartzPublic, BartzTutorial, nonfree},
    pages = {2625--2646},
    publisher = {ACM},
    rating = {0},
    title = {{Tuning and experimental analysis in evolutionary computation: what we still have wrong}},
    year = {2010},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/1830761.1830911},
    bdsk-url-2 = {http://dx.doi.org/10.1145/1830761.1830911}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss. The Sequential Parameter Optimization Toolbox. In Thomas Bartz-Beielstein, Marco Chiarandini, Luis Paquete, and Mike Preuss, editors, Experimental methods for the analysis of optimization algorithms, page 337–360. Springer, Berlin, Heidelberg, New York, 2010.
    [Bibtex]
    @incollection{Bart09f,
    abstract = {The sequential parameter optimization toolbox (SPOT) is one possible implementation of the SPO framework introduced in Chap. 2. It has been suc- cessfully applied to numerous heuristics for practical and theoretical optimization problems. We describe the mechanics and interfaces employed by SPOT to enable users to plug in their own algorithms. Furthermore, two case studies are presented to demonstrate how SPOT can be applied in practice, followed by a discussion of alternative metamodels to be plugged into it. We conclude with some general guidelines.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas and Lasarczyk, Christian and Preuss, Mike},
    booktitle = {Experimental Methods for the Analysis of Optimization Algorithms},
    date-added = {2015-11-29T01:38:58GMT},
    date-modified = {2017-03-08 23:03:28 +0000},
    doi = {10.1007/978-3-642-02538-9_14},
    editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    keywords = {bartzPublic, nonfree},
    pages = {337--360},
    publisher = {Springer},
    rating = {0},
    title = {{The Sequential Parameter Optimization Toolbox}},
    year = {2010},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-02538-9_14}}
  • [PDF] [DOI] Thomas Bartz-Beielstein and Mike Preuss. The future of experimental research. In Thomas Bartz-Beielstein, Marco Chiarandini, Lu{‘i}s Paquete, and Mike Preuss, editors, Experimental methods for the analysis of optimization algorithms, page 17–49. Springer berlin heidelberg, Berlin, Heidelberg, 2010.
    [Bibtex]
    @incollection{Bart09e,
    abstract = {In the experimental analysis of metaheuristic methods, two issues are still not sufficiently treated. Firstly, the performance of algorithms depends on their parametrizations---and of the parametrizations of the problem instances. However, these dependencies can be seen as means for understanding an algorithm's behavior. Secondly, the nondeterminism of evolutionary and other metaheuristic methods renders result distributions, not numbers.},
    address = {Berlin, Heidelberg},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Experimental Methods for the Analysis of Optimization Algorithms},
    date-added = {2021-07-23 22:44:07 +0200},
    date-modified = {2021-07-24 10:20:44 +0200},
    doi = {10.1007/978-3-642-02538-9_2},
    editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Lu{\'\i}s and Preuss, Mike},
    isbn = {978-3-642-02538-9},
    keywords = {bartzPublic, nonfree},
    pages = {17--49},
    publisher = {Springer Berlin Heidelberg},
    title = {The Future of Experimental Research},
    url = {https://doi.org/10.1007/978-3-642-02538-9_2},
    year = {2010},
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    bdsk-url-1 = {https://doi.org/10.1007/978-3-642-02538-9_2}}
  • [PDF] T. Bartz-Beielstein. SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization. Arxiv e-prints, jun 2010.
    [Bibtex]
    @article{Bart10w,
    abstract = {The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. SPOT includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. This article exemplifies how SPOT can be used for automatic and interactive tuning.},
    author = {Bartz-Beielstein, T},
    date-added = {2015-11-29T01:34:40GMT},
    date-modified = {2017-03-08 22:16:25 +0000},
    journal = {ArXiv e-prints},
    keywords = {bartzPublic, free},
    month = jun,
    rating = {0},
    title = {{SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization}},
    url = {http://arxiv.org/abs/1006.4645},
    year = {2010},
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    bdsk-url-1 = {http://arxiv.org/abs/1006.4645}}
  • [PDF] Thomas Bartz-Beielstein. SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization. Technical Report (CIOP), Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, jun 2010.
    [Bibtex]
    @techreport{Bart10e,
    abstract = {The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta- modeling approaches. This article exemplifies how spot can be used for automatic and interactive tuning.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    annote = {Comments: Related software can be downloaded from http://cran.r-project.org/web/packages/SPOT/index.html},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:32GMT},
    date-modified = {2018-11-16 20:11:54 +0100},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = jun,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization andData Mining)},
    rating = {0},
    title = {{SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization}},
    type = {Technical Report (CIOP)},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein, Oliver Flasch, Patrick Koch, and Wolfgang Konen. SPOT: A Toolbox for Interactive and Automatic Tuning of Search Heuristics and Simulation Models in the R Environment. Technical Report, TH Köln, 2010.
    [Bibtex]
    @techreport{Bart10q,
    abstract = {Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization (SPOT) package 1 for R is a toolbox for tuning and understanding simulation and optimization algorithms (Bartz-Beielstein et al., 2005; Bartz-Beielstein, 2010b). R is a freely available language and environment for statistical computing (R Development Core Team, 2008).
    SPOT was successfully applied to problems from theory and practice. It includes methods for interactive and automatic tuning. SPOT implements classical regression and analysis of variance techniques (Kleijnen, 1987, 2008); tree-based models such as CART and random forest (Breiman, 2001); Gaussian process models or Kriging (Sacks et al., 1989), and supports combinations of different meta-modeling approaches.},
    author = {Bartz-Beielstein, Thomas and Flasch, Oliver and Koch, Patrick and Konen, Wolfgang},
    date-added = {2015-11-29T01:38:39GMT},
    date-modified = {2017-03-08 22:13:36 +0000},
    institution = {TH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{SPOT: A Toolbox for Interactive and Automatic Tuning of Search Heuristics and Simulation Models in the R Environment}},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein, O. Flasch, P. Koch, and W. Konen. SPOT: A Toolbox for Interactive and Automatic Tuning in the R Environment. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 20. workshop computational intelligence, page 264–273. Universitätsverlag karlsruhe, 2010.
    [Bibtex]
    @inproceedings{Bart10p,
    abstract = {Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search al- gorithms. It includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and ran- dom forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. The suitability of these different meta models for parameter tuning is analyzed in this article. Automated and interactive approaches are compared..},
    author = {Bartz-Beielstein, Thomas and Flasch, O and Koch, P and Konen, W},
    booktitle = {Proceedings 20. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:38:42GMT},
    date-modified = {2017-03-08 22:12:30 +0000},
    editor = {Hoffmann, F and H{\"u}llermeier, E},
    keywords = {bartzPublic, free},
    pages = {264--273},
    publisher = {Universit{\"a}tsverlag Karlsruhe},
    rating = {0},
    title = {{SPOT: A Toolbox for Interactive and Automatic Tuning in the R Environment}},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein. Sequential Parameter Optimization–-An Annotated Bibliography. Technical Report, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, apr 2010.
    [Bibtex]
    @techreport{Bart10b,
    abstract = {This report collects more than one hundred publications related to the sequential parameter optimization, which was developed by Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss over the last years. Sequential parameter optimization can be described as a tuning algorithm with the following properties: (i) Use the available budget (e.g., simulator runs, number of function evaluations) sequentially, i.e., use information from the exploration of the search space to guide the search by building one or several meta models. Choose new design points based on pre- dictions from the meta model(s). Refine the meta model(s)) stepwise to improve knowledge about the search space. (ii) Try to cope with noise by improving confidence. Guarantee comparable confidence for search points. (iii) Collect information to learn from this tuning process, e.g., apply ex- plorative data analysis. (iv) Provide mechanisms both for interactive and automated tuning.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:31GMT},
    date-modified = {2017-03-07 23:45:58 +0000},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = apr,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Sequential Parameter Optimization---An Annotated Bibliography}},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein. Performing Experiments Using the Sequential Parameter Optimization Toolbox SPOT. Technical Report, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, jul 2010.
    [Bibtex]
    @techreport{Bart10m,
    abstract = {The sequential parameter optimization (spot) package for R (R De- velopment Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parame- ter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta- modeling approaches. This article exemplifies how experiments can be performed using the spot framework.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:32GMT},
    date-modified = {2017-03-07 23:18:38 +0000},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = jul,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Performing Experiments Using the Sequential Parameter Optimization Toolbox SPOT}},
    year = {2010},
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  • [PDF] Thomas Bartz-Beielstein, Mike Preuss, and Hans–Paul Schwefel. Model Optimization with Evolutionary Algorithms. In K. Lucas and P. Roosen, editors, Emergence, analysis, and evolution of structures–-concepts and strategies across disciplines, page 47–62. Springer, Berlin, Heidelberg, New York, 2010.
    [Bibtex]
    @incollection{Bart10f,
    abstract = {Does one need more than one optimization method? Or, stated differently, is there an optimal optimization method? Following from the No Free Lunch theorem (NFL, Wolpert and Macready [1]), in the general case---without clearly specified task---there is not. For every single task, creating a specialized method would be advantageous. Unfortunately, this requires (i) a lot of effort, and (ii) extensive knowledge about the treated problem, and is thus not practiced. Alter- natively, two strategies are usually followed when tackling a `new' optimization problem:
    -- Adapt an existing algorithm to the problem in its current form, and/or -- model/formulate the problem appropriately for an existing algorithm.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Schwefel, Hans--Paul},
    booktitle = {Emergence, Analysis, and Evolution of Structures---Concepts and Strategies Across Disciplines},
    date-added = {2015-11-29T01:39:07GMT},
    date-modified = {2017-03-07 22:09:40 +0000},
    editor = {Lucas, K and Roosen, P},
    keywords = {bartzPublic, nonfree},
    pages = {47--62},
    publisher = {Springer},
    rating = {0},
    title = {{Model Optimization with Evolutionary Algorithms}},
    year = {2010},
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  • [PDF] [DOI] Thomas Bartz-Beielstein, Marco Chiarandini, Luis Paquete, and Mike Preuss. Introduction–-Experimental Methods for the Analysis of Optimization Algorithms. In Thomas Bartz-Beielstein, Marco Chiarandini, Luis Paquete, and Mike Preuss, editors, Experimental methods for the analysis of optimization algorithms, page 1–13. Springer, Berlin, Heidelberg, New York, 2010.
    [Bibtex]
    @incollection{Bart10d,
    abstract = {Theory and experiments are complementary ways to analyze optimiza- tion algorithms. Experiments can also live a life of their own and produce learning without need to follow or test a theory. Yet, in order to make conclusions based on experiments trustworthy, reliable, and objective a systematic methodology is needed. In the natural sciences, this methodology relies on the mathematical frame- work of statistics. This book collects the results of recent research that focused on the application of statistical principles to the specific task of analyzing optimization algorithms.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    booktitle = {Experimental Methods for the Analysis of Optimization Algorithms},
    date-added = {2015-11-29T01:38:42GMT},
    date-modified = {2017-03-07 21:55:20 +0000},
    doi = {10.1007/978-3-642-02538-9_1},
    editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    isbn = {978-3-642-02537-2},
    keywords = {bartzPublic, nonfree},
    pages = {1--13},
    publisher = {Springer},
    rating = {0},
    title = {{Introduction---Experimental Methods for the Analysis of Optimization Algorithms}},
    uri = {\url{papers3://publication/doi/10.1007/978-3-642-02538-9_1}},
    year = {2010},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-02538-9_1}}
  • [PDF] Thomas Bartz-Beielstein. Writing Interfaces for the Sequential Parameter Optimization Toolbox SPOT. Technical Report, FH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 07 2010.
    [Bibtex]
    @techreport{Bart10n,
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:32GMT},
    date-modified = {2021-07-24 10:22:30 +0200},
    institution = {FH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = 07,
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Writing Interfaces for the Sequential Parameter Optimization Toolbox SPOT}},
    year = {2010},
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  • [PDF] O. Flasch, Thomas Bartz-Beielstein, P. Koch, and W. Konen. Clustering Based Niching for Genetic Programming in the R Environment. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 20. workshop computational intelligence, page 33–46. Universitätsverlag karlsruhe, 2010.
    [Bibtex]
    @inproceedings{Flas10f,
    abstract = {In this paper, we give a short introduction into RGP, a new genetic programming (GP) system based on the statistical package R. The system implements classical un- typed tree-based genetic programming as well as more advanced variants including, for example, strongly typed genetic programming and Pareto genetic programming. The main part of this paper is concerned with the problem of premature convergence of GP populations, accompanied by a loss of genetic diversity, resulting in poor ef- fectiveness of the search. We propose a clustering based niching approach to mitigate this problem. The results of preliminary experiments confirm that clustering based niching is effective in preserving genetic diversity in GP populations.
    },
    author = {Flasch, O and Bartz-Beielstein, Thomas and Koch, P and Konen, W},
    booktitle = {Proceedings 20. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:40:10GMT},
    date-modified = {2017-03-06 22:51:28 +0000},
    editor = {Hoffmann, F and H{\"u}llermeier, E},
    keywords = {bartzPublic, free},
    pages = {33--46},
    publisher = {Universit{\"a}tsverlag Karlsruhe},
    rating = {0},
    title = {{Clustering Based Niching for Genetic Programming in the R Environment}},
    year = {2010},
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  • [PDF] Oliver Flasch, Olaf Mersmann, and Thomas Bartz-Beielstein. RGP: An Open Source Genetic Programming System for the R Environment. In Martin Pelikan and Jürgen Branke, editors, Genetic and evolutionary computation conference, gecco 2010, proceedings, portland, oregon, page 2071–2072. Acm, 2010.
    [Bibtex]
    @inproceedings{Flas10d,
    abstract = {RGP is a new genetic programming system based on the R environment. The system implements classical untyped tree-based genetic programming as well as more advanced variants including, for example, strongly typed genetic pro- gramming and Pareto genetic programming. It strives for high modularity through a consistent architecture that al- lows the customization and replacement of every algorithm component, while maintaining accessibility for new users by adhering to the ``convention over configuration'' principle. Typical GP applications are supported by standard R in- terfaces. For example, symbolic regression via GP is sup- ported by the same ``formula interface'' as linear regression in R. RGP is freely available as an open source R package.},
    author = {Flasch, Oliver and Mersmann, Olaf and Bartz-Beielstein, Thomas},
    booktitle = {Genetic and Evolutionary Computation Conference, GECCO 2010, Proceedings, Portland, Oregon},
    date-added = {2015-11-29T01:40:15GMT},
    date-modified = {2017-03-07 23:33:48 +0000},
    editor = {Pelikan, Martin and Branke, J{\"u}rgen},
    keywords = {bartzPublic, nonfree},
    pages = {2071--2072},
    publisher = {ACM},
    rating = {0},
    title = {{RGP: An Open Source Genetic Programming System for the R Environment}},
    year = {2010},
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  • [PDF] [DOI] O. Flasch, Thomas Bartz-Beielstein, A. Davtyan, P. Koch, W. Konen, T. D. Oyetoyan, and M. Tamutan. Comparing SPO-tuned GP and NARX prediction models for stormwater tank fill level prediction. In Gary et al Fogel, editor, Proc. ieee congress evolutionary computation (cec), page 1579–1586. Ieee, 2010.
    [Bibtex]
    @inproceedings{Flas10e,
    abstract = {The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches, e.g. neuro- evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior results. By optimizing GP parameters, GP runtime can be significantly reduced without degrading result quality. The SPO-based parameter tuning leads to results with significantly lower standard deviation as compared to the GA based parameter tuning. Our methodol- ogy can be transferred to other optimization and simulation problems, where complex models have to be tuned.},
    author = {Flasch, O and Bartz-Beielstein, Thomas and Davtyan, A and Koch, P and Konen, W and Oyetoyan, T D and Tamutan, M},
    booktitle = {Proc. IEEE Congress Evolutionary Computation (CEC)},
    date-added = {2015-11-29T01:40:12GMT},
    date-modified = {2017-03-07 09:10:08 +0000},
    doi = {10.1109/CEC.2010.5586172},
    editor = {Fogel, Gary et al},
    isbn = {978-1-4244-6909-3},
    keywords = {bartzPublic, nonfree},
    pages = {1579--1586},
    publisher = {IEEE},
    rating = {0},
    title = {{Comparing SPO-tuned GP and NARX prediction models for stormwater tank fill level prediction}},
    year = {2010},
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    bdsk-url-1 = {http://dx.doi.org/10.1109/CEC.2010.5586172}}
  • [PDF] Oliver Flasch, Thomas Bartz-Beielstein, Artur Davtyan, Patrick Koch, Wolfgang Konen, Tosin Daniel Oyetoyan, and Michael Tamutan. Comparing Computational Intelligence Methods for Prediction Models in Environmental Engineering (Preprint). Technical Report, Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany, 2010.
    [Bibtex]
    @techreport{Flas10a,
    abstract = {The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e. Neural Networks (NN) and Genetic Program- ming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches, e.g. neuro-evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior results. By optimizing GP parameters, GP runtime can be significantly reduced without degrad- ing result quality. The SPO-based parameter tuning leads to results with significantly lower standard deviation as compared to the GA-based parameter tuning. Our methodology can be transferred to other optimization and simulation problems, where complex models have to be tuned.},
    address = {Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany},
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas and Davtyan, Artur and Koch, Patrick and Konen, Wolfgang and Oyetoyan, Tosin Daniel and Tamutan, Michael},
    date-added = {2015-11-29T01:40:14GMT},
    date-modified = {2017-03-07 09:06:48 +0000},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = feb,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Comparing Computational Intelligence Methods for Prediction Models in Environmental Engineering (Preprint)}},
    year = {2010},
    bdsk-file-1 = {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}}
  • [PDF] Oliver Flasch, Thomas Bartz-Beielstein, Artur Davtyan, Patrick Koch, Wolfgang Konen, Tosin Daniel Oyetoyan, and Michael Tamutan. Comparing CI Methods for Prediction Models in Environmental Engineering. Technical Report, Fachhochschule Köln, 2010.
    [Bibtex]
    @techreport{Flas10c,
    abstract = {The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e. Neural Networks (NN) and Genetic Program- ming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches, e.g. neuro-evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior results. By optimizing GP parameters, GP runtime can be significantly reduced without degrad- ing result quality. The SPO-based parameter tuning leads to results with significantly lower standard deviation as compared to the GA-based parameter tuning. Our methodology can be transferred to other optimization and simulation problems, where complex models have to be tuned.},
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas and Davtyan, Artur and Koch, Patrick and Konen, Wolfgang and Oyetoyan, Tosin Daniel and Tamutan, Michael},
    date-added = {2015-11-29T01:35:28GMT},
    date-modified = {2017-03-07 09:05:26 +0000},
    institution = {Fachhochschule K{\"o}ln},
    keywords = {bartzPublic, free},
    rating = {0},
    title = {{Comparing CI Methods for Prediction Models in Environmental Engineering}},
    year = {2010},
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  • [PDF] Frank Hutter, Thomas Bartz-Beielstein, Holger Hoos, Kevin Leyton-Brown, and Kevin P. Murphy. Sequential Model-Based Parameter Optimisation: an Experimental Investigation of Automated and Interactive Approaches. In Thomas Bartz-Beielstein, Marco Chiarandini, Luis Paquete, and Mike Preuss, editors, Experimental methods for the analysis of optimization algorithms, page 361–414. Springer, Berlin, Heidelberg, New York, 2010.
    [Bibtex]
    @incollection{Hutt09a,
    abstract = {This work experimentally investigates model-based approaches for opti- mizing the performance of parameterized randomized algorithms. Such approaches build a response surface model and use this model for finding good parameter set- tings of the given algorithm. We evaluated two methods from the literature that are based on Gaussian process models: sequential parameter optimization (SPO) (Bartz-Beielstein et al. 2005) and sequential Kriging optimization (SKO) (Huang et al. 2006). SPO performed better ``out-of-the-box,'' whereas SKO was competitive when response values were log transformed. We then investigated key design de- cisions within the SPO paradigm, characterizing the performance consequences of each. Based on these findings, we propose a new version of SPO, dubbed SPO+, which extends SPO with a novel intensification procedure and a log-transformed objective function. In a domain for which performance results for other (model- free) parameter optimization approaches are available, we demonstrate that SPO+ achieves state-of-the-art performance. Finally, we compare this automated param- eter tuning approach to an interactive, manual process that makes use of classical regression techniques. This interactive approach is particularly useful when only a relatively small number of parameter configurations can be evaluated. Because it can relatively quickly draw attention to important parameters and parameter interactions, it can help experts gain insights into the parameter response of a given algorithm and identify reasonable parameter settings. },
    address = {Berlin, Heidelberg, New York},
    author = {Hutter, Frank and Bartz-Beielstein, Thomas and Hoos, Holger and Leyton-Brown, Kevin and Murphy, Kevin P},
    booktitle = {Experimental Methods for the Analysis of Optimization Algorithms},
    date-added = {2015-11-29T01:40:47GMT},
    date-modified = {2019-08-06 21:58:24 +0200},
    editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    keywords = {bartzPublic, nonfree, Bart19g},
    pages = {361--414},
    publisher = {Springer},
    rating = {0},
    title = {{Sequential Model-Based Parameter Optimisation: an Experimental Investigation of Automated and Interactive Approaches}},
    year = {2010},
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  • [PDF] Patrick Koch, Wolfgang Konen, Oliver Flasch, and Thomas Bartz-Beielstein. Optimizing Support Vector Machines for Stormwater Prediction. Algorithm Engineering Report TR10-2-007, TU Dortmund, TU Dortmund, 08 2010.
    [Bibtex]
    @techreport{Koch10b,
    abstract = {In water resource management, efficient controllers of stormwater tanks prevent
    flooding of sewage systems, which reduces environmental pollution. With accurate
    predictions of stormwater tank fill levels based on past rainfall, such controlling systems are
    able to detect state changes as early as possible. Up to now, good results on this problem could only be achieved by applying special-purpose models especially designed for stormwater prediction. The question
    arises whether it is possible to replace such specialized models with state-of-the-art machine
    learning methods, such as Support Vector Machines (SVM) in combination with consequent parameter tuning
    using sequential parameter optimization, to achieve competitive performance. This study shows that even superior results can be obtained if the SVM hyperparameters and the considered preprocessing is
    tuned. Unfortunately, this tuning might also result in overfitting or oversearching -- both effects would lead to
    declined model generalizability. We analyze our tuned models and present possibilities to circumvent the effects of overfitting and oversearching.
    },
    address = {TU Dortmund},
    author = {Koch, Patrick and Konen, Wolfgang and Flasch, Oliver and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010},
    date-added = {2015-11-29T01:41:00GMT},
    date-modified = {2021-07-25 21:47:23 +0200},
    editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    institution = {TU Dortmund},
    isbn = {1864-4503},
    keywords = {bartzPublic, free},
    month = {08},
    number = {TR10-2-007},
    pages = {47--59},
    rating = {0},
    title = {{Optimizing Support Vector Machines for Stormwater Prediction}},
    type = {Algorithm Engineering Report},
    year = {2010},
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  • [PDF] Patrick Koch, Wolfgang Konen, Oliver Flasch, and Thomas Bartz-Beielstein. Optimization of Support Vector Regression Models for Stormwater Prediction. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 20. workshop computational intelligence, page 146–160. Universitätsverlag karlsruhe, 2010.
    [Bibtex]
    @inproceedings{Koch10c,
    abstract = {In this paper we propose a solution to a real-world time series regression problem: the prediction of fill levels of stormwater tanks. Our regression model is based on Support Vector Regression (SVR), but can easily be replaced with other data mining methods. The main intention of the work is to overcome frequently occuring problems in data mining by automatically tuning both preprocessing and hyperparameters. We highly believe that many models can be improved by a systematic preprocessing and hyperparameter tuning. The optimization of our model is presented in a step-by-step manner which can easily be adapted to other time series problems. We point out possible issues of parameter tuning, e.g., we analyze our tuned models with respect to overfitting and oversearching (which are effects that might lead to a reduced model generalizability) and present methods to circumvent such issues.
    },
    author = {Koch, Patrick and Konen, Wolfgang and Flasch, Oliver and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings 20. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:41:04GMT},
    date-modified = {2017-03-07 22:59:37 +0000},
    editor = {Hoffmann, F and H{\"u}llermeier, E},
    keywords = {bartzPublic, free},
    pages = {146--160},
    publisher = {Universit{\"a}tsverlag Karlsruhe},
    rating = {0},
    title = {{Optimization of Support Vector Regression Models for Stormwater Prediction}},
    year = {2010},
    bdsk-file-1 = {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}}
  • [PDF] Wolfgang Konen, Patrick Koch, Oliver Flasch, and Thomas Bartz-Beielstein. Parameter-Tuned Data Mining: A General Framework. In F. Hoffmann and E. Hüllermeier, editors, Proceedings 20. workshop computational intelligence. Universitätsverlag karlsruhe, 2010.
    [Bibtex]
    @inproceedings{Kone10a,
    abstract = {Real-world data mining applications often confront us with complex and noisy data, which makes it necessary to optimize the data mining models thoroughly to achieve high-quality results. We describe in this contribution an approach to tune the parameters of the model and the feature selection conjointly. The aim is to use one framework to solve a variety of tasks. We show that tuning is of large importance for high-quality results in bench- mark tasks like the Data Mining Cup: tuned models achieve rank 2 or 4 in the ranking tables, where the untuned model had rank 21 out of 67. We dis- cuss several issues of special relevance for the tuning of data mining models, namely resampling strategies and oversearching.},
    author = {Konen, Wolfgang and Koch, Patrick and Flasch, Oliver and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings 20. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:41:09GMT},
    date-modified = {2017-03-07 23:07:16 +0000},
    editor = {Hoffmann, F and H{\"u}llermeier, E},
    keywords = {bartzPublic, free},
    publisher = {Universit{\"a}tsverlag Karlsruhe},
    rating = {0},
    title = {{Parameter-Tuned Data Mining: A General Framework}},
    year = {2010},
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  • [PDF] [DOI] Jörg Ziegenhirt, Thomas Bartz-Beielstein, Oliver Flasch, Wolfgang Konen, and Martin Zaefferer. Optimization of Biogas Production with Computational Intelligence–-A Comparative Study. In Gary et al Fogel, editor, Proc. 2010 congress on evolutionary computation (cec’10) within ieee world congress on computational intelligence (wcci’10), barcelona, spain, page 3606–3613, Piscataway NJ, 2010. Ieee press.
    [Bibtex]
    @inproceedings{Zieg10b,
    abstract = {Biogas plants are reliable sources of energy based on renewable materials including organic waste. There is a high demand from industry to run these plants efficiently, which leads to a highly complex simulation and optimization problem. A comparison of several algorithms from computational intelli- gence to solve this problem is presented in this study. The sequential parameter optimization was used to determine improved parameter settings for these algorithms in an automated manner. We demonstrate that genetic algorithm and particle swarm optimization based approaches were outperformed by differ- ential evolution and covariance matrix adaptation evolution strategy. Compared to previously presented results, our approach required only one tenth of the number of function evaluations.},
    address = {Piscataway NJ},
    author = {Ziegenhirt, J{\"o}rg and Bartz-Beielstein, Thomas and Flasch, Oliver and Konen, Wolfgang and Zaefferer, Martin},
    booktitle = {Proc. 2010 Congress on Evolutionary Computation (CEC'10) within IEEE World Congress on Computational Intelligence (WCCI'10), Barcelona, Spain},
    date-added = {2015-11-29T01:43:46GMT},
    date-modified = {2017-03-07 22:57:56 +0000},
    doi = {10.1109/CEC.2010.5586509},
    editor = {Fogel, Gary et al},
    isbn = {978-1-4244-6909-3},
    keywords = {bartzPublic, nonfree},
    pages = {3606--3613},
    publisher = {IEEE Press},
    rating = {0},
    title = {{Optimization of Biogas Production with Computational Intelligence---A Comparative Study}},
    uri = {\url{papers3://publication/doi/10.1109/CEC.2010.5586509}},
    year = {2010},
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    bdsk-url-1 = {http://dx.doi.org/10.1109/CEC.2010.5586509}}
  • [PDF] Jörg Ziegenhirt, Thomas Bartz-Beielstein, Oliver Flasch, Wolfgang Konen, and Martin Zaefferer. Optimization of Biogas Production with Computational Intelligence – A Comparative Study (Preprint). Technical Report, TH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2010.
    [Bibtex]
    @techreport{Zieg10a,
    abstract = {Biogas plants are reliable sources of energy based on renewable materials including organic waste. There is a high demand from industry to run these plants efficiently, which leads to a highly complex simulation and optimization problem. A comparison of several algorithms from computational intelli- gence to solve this problem is presented in this study. The sequential parameter optimization was used to determine improved parameter settings for these algorithms in an automated manner. We demonstrate that genetic algorithm and particle swarm optimization based approaches were outperformed by differ- ential evolution and covariance matrix adaptation evolution strategy. Compared to previously presented results, our approach required only one tenth of the number of function evaluations.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Ziegenhirt, J{\"o}rg and Bartz-Beielstein, Thomas and Flasch, Oliver and Konen, Wolfgang and Zaefferer, Martin},
    date-added = {2015-11-29T01:43:42GMT},
    date-modified = {2017-03-07 22:57:15 +0000},
    institution = {TH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = mar,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Optimization of Biogas Production with Computational Intelligence - A Comparative Study (Preprint)}},
    year = {2010},
    bdsk-file-1 = {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}}

2009

  • [DOI] Thomas Bartz-Beielstein and Mike Preuss. The future of experimental research (tutorial). In Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers, GECCO ’09, page 3185–3226, New York, NY, USA, 2009. Acm.
    [Bibtex]
    @inproceedings{Bart09m,
    acmid = {1570417},
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers},
    date-added = {2017-01-14 16:21:42 +0000},
    date-modified = {2017-03-08 23:01:46 +0000},
    doi = {10.1145/1570256.1570417},
    isbn = {978-1-60558-505-5},
    keywords = {bartzPublic, BartzTutorial, nonfree, evolutionary algorithms, experimental analysis, parameter tuning, performance analysis, tutorial, BartzTutorial},
    location = {Montreal},
    numpages = {42},
    pages = {3185--3226},
    publisher = {ACM},
    series = {GECCO '09},
    title = {The Future of Experimental Research (Tutorial)},
    url = {http://doi.acm.org/10.1145/1570256.1570417},
    year = {2009},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://doi.acm.org/10.1145/1570256.1570417},
    bdsk-url-2 = {http://dx.doi.org/10.1145/1570256.1570417}}
  • [PDF] Thomas Bartz-Beielstein. Sequential Parameter Optimization. In Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner, editors, Sampling-based optimization in the presence of uncertainty, pages 1-32, Dagstuhl, Germany, 2009. Schloss dagstuhl – leibniz-zentrum fuer informatik, germany.
    [Bibtex]
    @inproceedings{Bart09c,
    abstract = {We provide a comprehensive, effective and very efficient method- ology for the design and experimental analysis of algorithms. We rely on modern statistical techniques for tuning and understanding algorithms from an experimental perspective. Therefore, we make use of the sequential pa- rameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical op- timization problems. Two case studies, which illustrate the applicability of SPO to algorithm tuning and model selection, are presented.},
    address = {Dagstuhl, Germany},
    annote = {Keywords: Optimization, evolutionary algorithms, design of experiments},
    author = {Bartz-Beielstein, Thomas},
    booktitle = {Sampling-based Optimization in the Presence of Uncertainty},
    date-added = {2015-11-29T01:38:24GMT},
    date-modified = {2017-03-07 23:37:53 +0000},
    editor = {Branke, J{\"u}rgen and Nelson, Barry L and Powell, Warren Buckler and Santner, Thomas J},
    keywords = {bartzPublic, free},
    pages = {1-32},
    publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
    rating = {0},
    title = {{Sequential Parameter Optimization}},
    url = {http://drops.dagstuhl.de/opus/volltexte/2009/2115},
    year = {2009},
    bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAgLi4vc2NpZWJvL1dlYnN0b3JlLmQvYmFydDA5Yy5wZGZPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LYmFydDA5Yy5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpiYXJ0MDljLnBkZgAADgAYAAsAYgBhAHIAdAAwADkAYwAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL2JhcnQwOWMucGRmAAATAAEvAAAVAAIADP//AAAACAANABoAJABHAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAZc=},
    bdsk-url-1 = {http://drops.dagstuhl.de/opus/volltexte/2009/2115}}
  • [PDF] Oliver Flasch, Thomas Bartz-Beielstein, Patrick Koch, and Wolfgang Konen. Genetic Programming Applied to Predictive Control in Environmental Engineering. In Frank Hoffmann and Eyke Hüllermeier, editors, Proceedings 19. workshop computational intelligence, page 101–113, Karlsruhe, 2009. Kit scientific publishing.
    [Bibtex]
    @inproceedings{Flas09a,
    abstract = {We introduce a new hybrid Genetic Programming (GP) based method for time- series prediction in predictive control applications. Our method combines existing state-of-the-art analytical models from predictive control with a modern typed graph GP system. The main idea is to pre-structure the GP search space with existing analytical models to improve prediction accuracy. We apply our method to a difficult predictive control problem from the water resource management industry, yielding an improved prediction accuracy, compared with both the best analytical model and with a modern GP method for time series prediction. Even if we focus this first study on predictive control, the automatic optimization of existing models through GP shows a great potential for broader application.},
    address = {Karlsruhe},
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas and Koch, Patrick and Konen, Wolfgang},
    booktitle = {Proceedings 19. Workshop Computational Intelligence},
    date-added = {2015-11-29T01:40:13GMT},
    date-modified = {2017-03-07 21:41:32 +0000},
    editor = {Hoffmann, Frank and H{\"u}llermeier, Eyke},
    keywords = {bartzPublic, free},
    pages = {101--113},
    publisher = {KIT Scientific Publishing},
    rating = {0},
    title = {{Genetic Programming Applied to Predictive Control in Environmental Engineering}},
    year = {2009},
    bdsk-file-1 = {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}}
  • [PDF] Oliver Flasch and Thomas Bartz-Beielstein. Sequential parameter optimization applied to evolutionary strategies for portfolio optimization. In A. Colubi, E. J. Kontoghiorghes, D. S. G. Pollock, Van S. Aelst, and P. Winker, editors, Second workshop of the ercim working group on computing and statistics (ercim 09), page 107. European Research Consortium for Informatics and Mathematics,, oct 2009.
    [Bibtex]
    @inproceedings{Flas09b,
    author = {Flasch, Oliver and Bartz-Beielstein, Thomas},
    booktitle = {Second Workshop of the ERCIM Working Group on Computing and Statistics (ERCIM 09)},
    date-added = {2016-11-11 04:45:31 +0000},
    date-modified = {2017-01-14 15:20:33 +0000},
    editor = {A. Colubi and E.J. Kontoghiorghes and D.S.G. Pollock and S. Van Aelst and P. Winker},
    keywords = {bartzPublic, free},
    month = oct,
    organization = {European Research Consortium for Informatics and Mathematics},
    pages = {107},
    title = {{Sequential parameter optimization applied to evolutionary strategies for portfolio optimization}},
    year = {2009},
    bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAgLi4vc2NpZWJvL1dlYnN0b3JlLmQvZmxhczA5Yi5wZGZPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LZmxhczA5Yi5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpmbGFzMDliLnBkZgAADgAYAAsAZgBsAGEAcwAwADkAYgAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL2ZsYXMwOWIucGRmAAATAAEvAAAVAAIADP//AAAACAANABoAJABHAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAZc=}}
  • [DOI] Wolfgang Konen, Tobias Zimmer, and Thomas Bartz-Beielstein. Optimierte Modellierung von Füllständen in Regenüberlaufbecken mittels CI-basierter Parameterselektion Optimized Modelling of Fill Levels in Stormwater Tanks Using CI-based Parameter Selection Schemes. At – automatisierungstechnik, 57(3):155–166, 2009.
    [Bibtex]
    @article{kone09b,
    author = {Wolfgang Konen and Tobias Zimmer and Thomas Bartz-Beielstein},
    date-added = {2021-07-24 10:17:03 +0200},
    date-modified = {2021-07-24 10:19:35 +0200},
    doi = {doi:10.1524/auto.2009.0756},
    journal = {at - Automatisierungstechnik},
    keywords = {bartzPublic, nonfree},
    number = {3},
    pages = {155--166},
    publisher = {Oldenbourg},
    title = {{Optimierte Modellierung von F{\"u}llst{\"a}nden in Regen{\"u}berlaufbecken mittels CI-basierter Parameterselektion Optimized Modelling of Fill Levels in Stormwater Tanks Using CI-based Parameter Selection Schemes}},
    url = {https://doi.org/10.1524/auto.2009.0756},
    volume = {57},
    year = {2009},
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    bdsk-url-1 = {https://doi.org/10.1524/auto.2009.0756}}
  • [DOI] Wolfgang Konen, Tobias Zimmer, and Thomas Bartz-Beielstein. Optimized Modelling of Fill Levels in Stormwater Tanks Using CI-based Parameter Selection Schemes (in german). At-automatisierungstechnik, 57(3):155–166, 2009.
    [Bibtex]
    @article{Kone09beng,
    abstract = {Ziel dieses Beitrags ist die Prognose der F{\"u}llst{\"a}nde in Regen{\"u}berlaufbecken aufgrund von Regeneintrag und Bodenbeschaffenheit. Wir vergleichen verschiedene Prognoseverfahren und nutzen die Sequentielle Parameteroptimierung (SPO), um f{\"u}r jedes Verfahren in vergleichbarer Weise bestm{\"o}gliche Parameter zu finden. Es zeigt sich, dass diverse Standard- und CI-basierte Verfahren der Modellierung mit intermittierenden Regenmessdaten als Input nicht gut zurecht kommen. Problemspezifische Modellierungen, die kausale Effekte erster Ordnung ber{\"u}cksichtigen, erzielen wesentlich kleinere Prognosefehler. Wichtige Resultate dieser Arbeit sind: (i) SPO l{\"a}sst sich auf verschiedene Modellierungsverfahren gleicherma{\ss}en anwenden und automatisiert das manuell zeitaufwendige Parameter-Tuning, (ii) das beste manuell er- zielte Ergebnis wurde mit SPO nochmals um ca. 30% verbessert und (iii) SPO analysiert in konsistenter Weise den Einfluss von Parametern und erlaubt so oftmals eine zielgerichtete Vereinfachung oder Verbesserung des Modellentwurfs.
    The aim of this paper is the prediction of fill levels in stormwater tanks based on rain measurements and soil conditions. We compare different prediction methods and use sequential parameter optimization (SPO) to find in a comparable manner the best parameters for each method. Several standard and CI-based modeling methods show larger prediction errors when trained on rain data with strong intermittent and bursting beha- viour. Models developed specific to the problem show a smaller prediction error. Main results of our work are: (i) SPO is applicable to diverse forecasting methods and auto- mates the time-consuming parameter tuning, (ii) the best manual result achieved before was improved with SPO by 30% and (iii) SPO analyses in a consistent manner the para- meter influence and allows a purposeful simplification and/or refinement of the model design.},
    annote = {original title: Optimierte Modellierung von F{\"u}llst{\"a}nden in Regen{\"u}berlaufbecken mittels CI-basierter Parameterselektion},
    author = {Konen, Wolfgang and Zimmer, Tobias and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:41:08GMT},
    date-modified = {2021-07-24 10:18:03 +0200},
    doi = {10.1524/auto.2009.0756},
    journal = {at-Automatisierungstechnik},
    keywords = {bartzPublic, nonfree},
    number = {3},
    pages = {155--166},
    publisher = {Oldenbourg},
    rating = {0},
    title = {{Optimized Modelling of Fill Levels in Stormwater Tanks Using CI-based Parameter Selection Schemes (in german)}},
    url = {http://dx.doi.org/10.1524/auto.2009.0756},
    volume = {57},
    year = {2009},
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    bdsk-url-1 = {http://dx.doi.org/10.1524/auto.2009.0756}}
  • [DOI] Wolfgang Konen and Thomas Bartz-Beielstein. Reinforcement learning for games: failures and successes. In Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers, GECCO ’09, page 2641–2648, New York, NY, USA, 2009. Acm.
    [Bibtex]
    @inproceedings{Kone09a,
    abstract = {We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforce- ment learning tasks. In both cases these algorithms seek to op- timize a neural network which provides the policy for playing a simple game (TicTacToe). Our contribution is to study the effect of varying learning conditions on learning speed and quality. Certain initial failures with wrong fitness functions lead to the development of new fitness functions, which allow fast learning. These new fit- ness functions in combination with CMA-ES reduce the number of required games needed for training to the same order of magnitude as TDL.
    The selection of suitable features is also of critical importance for the learning success. It could be shown that using the raw board position as an input feature is not very effective -- and it is orders of magnitudes slower than different feature sets which exploit the symmetry of the game. We develop a measure ``feature set utility'', FU , which allows to characterize a given feature set in advance. We showthatthelowerboundprovidedbyFU islargelyinaccordance with the results from our repeated experiments for very different learning algorithms, CMA-ES and TDL.},
    acmid = {1570375},
    address = {New York, NY, USA},
    author = {Konen, Wolfgang and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers},
    date-added = {2016-11-11 05:09:01 +0000},
    date-modified = {2017-03-07 23:28:49 +0000},
    doi = {10.1145/1570256.1570375},
    isbn = {978-1-60558-505-5},
    keywords = {bartzPublic, nonfree, evolution strategies, failures, games, learning},
    location = {Montreal, Qu\&\#233;bec, Canada},
    numpages = {8},
    pages = {2641--2648},
    publisher = {ACM},
    series = {GECCO '09},
    title = {Reinforcement Learning for Games: Failures and Successes},
    url = {http://doi.acm.org/10.1145/1570256.1570375},
    year = {2009},
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    bdsk-url-1 = {http://doi.acm.org/10.1145/1570256.1570375},
    bdsk-url-2 = {http://dx.doi.org/10.1145/1570256.1570375}}
  • [PDF] Catalin Stoean, Mike Preuss, Thomas Bartz-Beielstein, and Ruxandra Stoean. A New Clustering-Based Evolutionary Algorithm for Real-Valued Multimodal Optimization. Technical Report, TU Dortmund, Dortmund, Germany, 2009.
    [Bibtex]
    @techreport{Stoe09a,
    abstract = {Solving multimodal optimization tasks (problems with multiple glo- bal/local optimal solutions) by the state-of-the-art evolutionary algorithms (EAs) presumes separation of a population of individuals into subpopulations, each con- nected to a different optimum, with the aim of maintaining diversity for a longer period of time. Instead of using the typical separation that uses depends on a ra- dius, present work proposes the employment of a clustering technique in order to distribute the candidate solutions to different species. Additionally, the proposed method corrects the separation by means of a mechanism that verifies the topo- logical placement of the individuals in the fitness landscape with the purpose of connecting each species to a different optimum. The best individuals from each subpopulation are preserved from one generation to another in order to assure the conservation of the species. The method is applied on a set of benchmark functions that exhibit various properties, under multiple parameter settings, and the results demonstrate its great potential, especially of coping with relatively difficult problems under a limited budget of fitness evaluations.},
    address = {TU Dortmund, Dortmund, Germany},
    author = {Stoean, Catalin and Preuss, Mike and Bartz-Beielstein, Thomas and Stoean, Ruxandra},
    date-added = {2015-11-29T01:43:16GMT},
    date-modified = {2017-03-06 22:19:49 +0000},
    keywords = {bartzPublic, free},
    publisher = {Chair of Algorithm Engineering},
    rating = {0},
    title = {{A New Clustering-Based Evolutionary Algorithm for Real-Valued Multimodal Optimization}},
    year = {2009},
    bdsk-file-1 = {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}}
  • [PDF] Ruxandra Stoean, Thomas Bartz-Beielstein, Mike Preuss, and Catalin Stoean. A Support Vector Machine-Inspired Evolutionary Approach for Parameter Setting in Metaheuristics. Technical Report, TH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2009.
    [Bibtex]
    @techreport{Stoe09b,
    abstract = {The paper presents a novel, combined methodology to target param- eter tuning. It uses Latin hypersquare sampling to generate a diverse, large set of configurations for the variables to be set. These serve as input for the metaheuristic to be tuned and a complete data set, with the parameter values and the success rate obtained by the algorithm, is formed. The collection is next subject to regression by means of a recent evolutionary engine for support vector machine learning. The investigations on tuning an evolutionary algorithm for function optimization led to interesting insights on a simple, unconstrained structural and coe$\pm$cient evolution of the underlying regression function. The approach can be further improved in pre- diction accuracy, while also enhanced to target multiobjectivity and discovery of the best set of performing parameters for the metaheuristic to be tuned.
    },
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Stoean, Ruxandra and Bartz-Beielstein, Thomas and Preuss, Mike and Stoean, Catalin},
    date-added = {2015-11-29T01:43:25GMT},
    date-modified = {2017-03-06 22:22:47 +0000},
    institution = {TH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = jan,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{A Support Vector Machine-Inspired Evolutionary Approach for Parameter Setting in Metaheuristics}},
    year = {2009},
    bdsk-file-1 = {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}}

2008

  • [PDF] Thomas Bartz-Beielstein and Wolfgang Konen. Datenanalyse und Prozessoptimierung am Beispiel Kläranlagen. Technical Report, FH Köln, 2008.
    [Bibtex]
    @techreport{Bart08h,
    abstract = {Ziel dieses Projekts ist die Erforschung von Prognosemodellen f{\"u}r F{\"u}llst{\"a}nde in Kl{\"a}ranlagen aufgrund von Regeneintrag und Bodenbeschaffenheit. Wir vergleichen verschiedene Prognoseverfahren und nutzen die Sequentielle Parameteroptimierung (SPO), um f{\"u}r jedes Prognosemodell in vergleichbarer Weise bestm{\"o}gliche Parameter zu finden. Mit SPO wird in konsistenter Weise der Einfluss von Parametern analysiert. So kommt es zu einer zielgerichteten Vereinfachung und Verbesserung des Modellentwurfs.
    },
    author = {Bartz-Beielstein, Thomas and Konen, Wolfgang},
    date-added = {2015-11-29T01:38:48GMT},
    date-modified = {2021-07-23 22:53:08 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Datenanalyse und Prozessoptimierung am Beispiel Kl{\"a}ranlagen}},
    year = {2008},
    bdsk-file-1 = {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}}
  • [DOI] Thomas Bartz-Beielstein and Mike Preuss. Experimental research in evolutionary computation–The Future of Experimental Research ( GECCO Tutorial 2008). In Conor Ryan and Maarten Keijzer, editors, Gecco (companion), page 2517–2534, New York, New York, USA, 2008. ACM, Genetic and evolutionary computation conf. (gecco 2008), atlanta, georgia, us.
    [Bibtex]
    @inproceedings{BP08a,
    address = {New York, New York, USA},
    affiliation = {ACM},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {GECCO (Companion)},
    date-added = {2017-01-14 16:23:11 +0000},
    date-modified = {2017-01-14 16:23:23 +0000},
    doi = {10.1145/1388969.1389066},
    editor = {Ryan, Conor and Keijzer, Maarten},
    isbn = {9781605581316},
    keywords = {bartzPublic, BartzTutorial},
    month = jul,
    organization = {ACM},
    pages = {2517--2534},
    publisher = {Genetic and Evolutionary Computation Conf. (GECCO 2008), Atlanta, Georgia, US},
    rating = {0},
    title = {{Experimental research in evolutionary computation--The Future of Experimental Research ( GECCO Tutorial 2008)}},
    url = {http://dx.doi.org/10.1145/1388969.1389066},
    year = {2008},
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    bdsk-url-1 = {http://dx.doi.org/10.1145/1388969.1389066}}
  • [PDF] Thomas Bartz-Beielstein and Wolfgang Konen. Genetisches Programmieren für Vorhersagemodelle in der Finanzwirtschaft. Technical Report, 2008.
    [Bibtex]
    @techreport{Bart08g,
    abstract = {Finanzzeitreihen k{\"o}nnen sich durch politische, {\"o}konomische und umweltbedingte Einfl{\"u}sse sehr rasch ver{\"a}ndern. Aus diesen Gr{\"u}nden lassen sich viele f{\"u}r die wirtschaftliche Praxis interessante Zeitreihen nur schlecht mit klassischen Prognoseverfahren vorhersagen. Ziel des Projekts ist die Entwicklung modularer Systeme zur Analyse und Prognose von Daten aus der Industrie und {\"o}konomie, insbesondere von multivariaten Zeitreihen, mit Verfahren der Computational Intelligence (CI), insbesondere dem Genetischen Programmieren (GP). Neben den Daten der Zeitreihen k{\"o}nnen f{\"u}r GP beliebige Eingabedaten wie z.B. Schl{\"u}sselw{\"o}rter aus B{\"o}rsennachrichten genutzt werden.},
    author = {Bartz-Beielstein, Thomas and Konen, Wolfgang},
    date-added = {2015-11-29T01:38:42GMT},
    date-modified = {2018-11-16 21:22:49 +0100},
    keywords = {bartzPublic, free},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Genetisches Programmieren f{\"u}r Vorhersagemodelle in der Finanzwirtschaft}},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Thomas Bartz-Beielstein. How Experimental Algorithmics Can Benefit from Mayo’s Extensions to Neyman-Pearson Theory of Testing. Synthese, 163(3):385–396, 2008.
    [Bibtex]
    @article{Bart08a,
    abstract = {
    Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Experimentation is necessary. The analysis of algorithms should follow the same principles and standards of other empirical sciences. This article focuses on stochastic search algorithms, such as evolutionary algorithms or particle swarm optimization. Stochastic search algorithms tackle hard real-world optimization problems, e.g., problems from chemical engineering, airfoil optimization, or bioinformatics, where classical methods from mathematical optimization fail.
    Nowadays statistical tools that are able to cope with problems like small sample sizes, non-normal distributions, noisy results, etc. are developed for the analysis of algorithms. Although there are adequate tools to discuss the statistical significance of experimental data, statistical significance is not scientifically meaningful per se. It is necessary to bridge the gap between the statistical significance of an experimental result and its scientific meaning. We will propose some ideas on how to accomplish this task based on Mayo's learning model (NPT*).},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:36GMT},
    date-modified = {2018-11-16 21:21:21 +0100},
    doi = {10.1007/s11229-007-9297-z},
    journal = {Synthese},
    keywords = {bartzPublic, nonfree},
    language = {English},
    number = {3},
    pages = {385--396},
    rating = {0},
    title = {{How Experimental Algorithmics Can Benefit from Mayo's Extensions to Neyman-Pearson Theory of Testing}},
    url = {http://link.springer.com/10.1007/s11229-007-9297-z},
    volume = {163},
    year = {2008},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://link.springer.com/10.1007/s11229-007-9297-z},
    bdsk-url-2 = {http://dx.doi.org/10.1007/s11229-007-9297-z}}
  • [PDF] Thomas Bartz-Beielstein and Wolfgang Konen. Moderne statistische Verfahren zur Parameteroptimierung und systematischen Modellauswahl. Forschungsbericht, FH Köln, 2008.
    [Bibtex]
    @techreport{Bart08f,
    abstract = {Die Simulation komplexer technischer Vorg{\"a}nge kann mit unterschiedlichen Modellen durchgef{\"u}hrt werden. Meistens erfolgt die Modellauswahl und anschlie{\ss}ende Parametrisierung des Modells nach subjektiven Kriterien. Wir demonstrieren, wie eine systematische Vorgehensweise zur Modellselektion mittels SPO (sequentieller Parameteroptimierung) diesen Vorgang objektivieren kann. Diese Vorgehensweise konnte erfolgreich zur Auswahl eines Modells zur Vorhersage von F{\"u}llst{\"a}nden in Regen{\"u}berlaufbecken angewandt werden.},
    author = {Bartz-Beielstein, Thomas and Konen, Wolfgang},
    date-added = {2015-11-29T01:38:40GMT},
    date-modified = {2021-07-24 10:14:38 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {Cologne University of Applied Sciences},
    rating = {0},
    title = {{Moderne statistische Verfahren zur Parameteroptimierung und systematischen Modellauswahl}},
    type = {Forschungsbericht},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Tobias Zimmer, and Wolfgang Konen. Parameterselektion für komplexe Modellierungsaufgaben der Wasserwirtschaft – Moderne CI-Verfahren zur Zeitreihenanalyse. In R. Mikut and M. Reischl, editors, Proc. 18th workshop computational intelligence, page 136–150. Universitätsverlag, karlsruhe, 2008.
    [Bibtex]
    @inproceedings{Bart08c,
    abstract = {Die Simulation komplexer technischer Vorg{\"a}nge kann mit unterschiedlichen Mo- dellen durchgef{\"u}hrt werden. Meistens erfolgt die Modellauswahl und anschlie{\ss}ende Parametrisierung des Modells nach subjektiven Kriterien. Wir demonstrieren, wie eine systematische Vorgehensweise zur Modellselektion mittels SPO (sequentieller Parameteroptimierung) diesen Vorgang objektivieren kann. Um die einzelnen Schrit- te nachvollziehbar darzustellen, basiert unsere Darstellung auf einem Beispiel aus der Praxis: der Modellierung von F{\"u}llst{\"a}nden in Regen{\"u}berlaufbecken.
    },
    author = {Bartz-Beielstein, Thomas and Zimmer, Tobias and Konen, Wolfgang},
    booktitle = {Proc. 18th Workshop Computational Intelligence},
    date-added = {2015-11-29T01:39:31GMT},
    date-modified = {2017-03-07 23:06:11 +0000},
    editor = {Mikut, R and Reischl, M},
    keywords = {bartzPublic, free},
    pages = {136--150},
    publisher = {Universit{\"a}tsverlag, Karlsruhe},
    rating = {0},
    title = {{Parameterselektion f{\"u}r komplexe Modellierungsaufgaben der Wasserwirtschaft -- Moderne CI-Verfahren zur Zeitreihenanalyse}},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein and Mike Preuss. Preprint: The Future of Experimental Research (Tutorial). 10th International Conference on Parallel Problem Solving From Nature (PPSN), Dortmund, sep 2008.
    [Bibtex]
    @misc{BP08blec,
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    date-added = {2015-11-29T01:39:02GMT},
    date-modified = {2017-01-14 15:12:55 +0000},
    howpublished = {10th International Conference on Parallel Problem Solving From Nature (PPSN), Dortmund},
    issn = {2191-365X},
    keywords = {bartzPublic, BartzTutorial},
    month = sep,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{Preprint: The Future of Experimental Research (Tutorial)}},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein. Review: Design and Analysis of Simulation Experiments by Jack P.C. Kleijnen. Informs computing society news, 2:11–14, 8 2008.
    [Bibtex]
    @article{Bart08d,
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:40GMT},
    date-modified = {2017-01-14 14:46:38 +0000},
    journal = {INFORMS Computing Society News},
    keywords = {bartzPublic, free},
    month = 8,
    pages = {11--14},
    rating = {0},
    title = {{Review: Design and Analysis of Simulation Experiments by Jack P.C. Kleijnen}},
    volume = {2},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss. SPOT – Sequential Parameter Optimization Toolbox (MATLAB Documentation). Technical Report, FH Köln, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, jan 2008.
    [Bibtex]
    @techreport{Bart08i,
    abstract = {This article describes the SPO toolbox, which was implemented in MATLAB. Note, following SPOT versions will be implemented in R (Ihaka and Gentle- man, 1996). First versions of this software were written as small MATLAB scripts to analyse algorithm's performance. Over the years, the complexity of these scripts increased---but there was no time for a complete re-write, because many researchers used SPOT. A complete change would result in negative con- sequences for on-going research projects. The MATLAB version (currently v0.5) will be available, but new features will be added to the R version.
    SPO is an acronym for sequential parameter optimization (Bartz-Beielstein, 2006). The SPO toolbox described in this article is referred to as SPOT. It was developed over the last years by Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preu{\ss}. The main purpose of SPO is to determine improved param- eter settings for optimization algorithms to analyze and understand their per- formance. SPO was successfully applied to numerous optimization algorithms, especially in the field of evolutionary computation, i.e., evolution strategies, par- ticle swarm optimization, algorithmic chemistries etc.},
    address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
    author = {Bartz-Beielstein, Thomas and Lasarczyk, Christian and Preuss, Mike},
    date-added = {2015-11-29T01:38:58GMT},
    date-modified = {2021-07-23 22:54:18 +0200},
    institution = {FH K{\"o}ln},
    issn = {2191-365X},
    keywords = {bartzPublic, free},
    month = jan,
    publisher = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
    rating = {0},
    title = {{SPOT - Sequential Parameter Optimization Toolbox (MATLAB Documentation)}},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss. SPOT: Sequential Parameter Optimization Toolbox. Computational Intelligence – Collaborative Research Center 531 CI-256/08, TU Dortmund, Secretary of the SFB 531 – Technische Universität Dortmund · Dept. of Computer Science/LS 2 · 44221 Dortmund · Germany, 11 2008.
    [Bibtex]
    @techreport{Bart06f,
    address = {Secretary of the SFB 531 - Technische Universit{\"a}t Dortmund · Dept. of Computer Science/LS 2 · 44221 Dortmund · Germany},
    author = {Bartz-Beielstein, Thomas and Lasarczyk, Christian and Preuss, Mike},
    date-added = {2015-11-29T01:38:47GMT},
    date-modified = {2021-07-23 22:52:20 +0200},
    institution = {TU Dortmund},
    keywords = {bartzPublic, free},
    month = {11},
    number = {CI-256/08},
    publisher = {Universit{\"a}t Dortmund, Germany},
    rating = {0},
    title = {{SPOT: Sequential Parameter Optimization Toolbox}},
    type = {Computational Intelligence - Collaborative Research Center 531},
    year = {2008},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://sfbci.uni-dortmund.de/Publications/Reference/Downloads/25608.pdf}}
  • [PDF] Wolfgang Konen and Thomas Bartz-Beielstein. Internationaler DATA-MINING-CUP (DMC) mit studentischer Beteiligung des Campus Gummersbach. Forschungsbericht, Fachhochschule Köln, 2008.
    [Bibtex]
    @techreport{Kone08d,
    abstract = {Durch den internationalen Wettbewerb DATA-MINING-CUP (DMC) werden Studierende fr{\"u}hzeitig an aktuelle und praxisrelevante Themen aus Forschung und Entwicklung herangef{\"u}hrt. Die erstmalige Teilnahme von Studierenden des Campus Gummersbach am DMC verlief erfolgreich.},
    author = {Konen, Wolfgang and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:41:05GMT},
    date-modified = {2021-07-24 10:13:22 +0200},
    institution = {Fachhochschule K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{Internationaler DATA-MINING-CUP (DMC) mit studentischer Beteiligung des Campus Gummersbach}},
    type = {Forschungsbericht},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] Wolfgang Konen and Thomas Bartz-Beielstein. Reinforcement Learning für strategische Brettspiele. Forschungsbericht, FH Köln, 2008.
    [Bibtex]
    @techreport{Kone08c,
    abstract = {Reinforcement Learning (best{\"a}rkendes Lernen) ist eine wichtige Lernmethode f{\"u}r Anwendungen, in denen eine Belohnung erst zeitverz{\"o}gert erfolgt, wie es beispiels- weise in Brettspielen der Fall ist. Wir zeigen, dass es selbst f{\"u}r einfache Brettspiele sehr stark von den Merkmalen abh{\"a}ngt, ob und wie schnell ein Lernerfolg eintritt. Schlech ge- w{\"a}hlte Merkmale k{\"o}nnen den Lernprozess verhindern, geeignet gew{\"a}hlte Merkmale k{\"o}nnen ihn dagegen um den Faktor 100 beschleunigen.},
    author = {Konen, Wolfgang and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:41:08GMT},
    date-modified = {2021-07-24 10:13:54 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{Reinforcement Learning f{\"u}r strategische Brettspiele}},
    type = {Forschungsbericht},
    year = {2008},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Wolfgang Konen and Thomas Bartz-Beielstein. Reinforcement Learning: Insights from Interesting Failures in Parameter Selection. In Günter et al Rudolph, editor, Ppsn’2008: 10th international conference on parallel problem solving from nature, dortmund, page 478–487, Berlin, sep 2008. Springer.
    [Bibtex]
    @inproceedings{Kone08a,
    abstract = {We investigate reinforcement learning methods, namely the temporal difference learning TDL algorithm, on game-learning tasks. Small modifications in algorithm setup and parameter choice can have significant impact on success or failure to learn. We demonstrate that small differences in input features influence significantly the learning pro- cess. By selecting the right feature set we found good results within only 1/100 of the learning steps reported in the literature. Different metrics for measuring success in a reproducible manner are developed. We discuss why linear output functions are often preferable compared to sigmoid output functions. },
    address = {Berlin},
    author = {Konen, Wolfgang and Bartz-Beielstein, Thomas},
    booktitle = {PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund},
    date-added = {2015-11-29T01:41:09GMT},
    date-modified = {2018-11-16 21:36:51 +0100},
    doi = {10.1007/978-3-540-87700-4_48},
    editor = {Rudolph, G{\"u}nter et al},
    isbn = {978-3-540-87699-1},
    keywords = {bartzPublic, nonfree},
    month = sep,
    pages = {478--487},
    publisher = {Springer},
    rating = {0},
    title = {{Reinforcement Learning: Insights from Interesting Failures in Parameter Selection}},
    url = {http://dx.doi.org/10.1007/978-3-540-87700-4_48},
    year = {2008},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-540-87700-4_48}}

2007

  • [DOI] Thomas Bartz-Beielstein, María Blesa J. Aguilera, Christian Blum, Boris Naujoks, Andrea Roli, Günter Rudolph, and Michael Sampels, editors. Hybrid metaheuristics, 4th international workshop, HM 2007, dortmund, germany, october 8-9, 2007, proceedings, volume 4771 of Lecture Notes in Computer ScienceSpringer, 2007.
    [Bibtex]
    @proceedings{Bart07a,
    bibsource = {dblp computer science bibliography, http://dblp.org},
    biburl = {http://dblp2.uni-trier.de/rec/bib/conf/hm/2007},
    date-added = {2017-03-03 11:22:41 +0000},
    date-modified = {2021-07-23 22:56:13 +0200},
    doi = {10.1007/978-3-540-75514-2},
    editor = {Thomas Bartz-Beielstein and Mar{\'{\i}}a J. Blesa Aguilera and Christian Blum and Boris Naujoks and Andrea Roli and G{\"u}nter Rudolph and Michael Sampels},
    isbn = {978-3-540-75513-5},
    keywords = {bartzPublic, nonfree},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Wed, 25 May 2016 10:19:57 +0200},
    title = {Hybrid Metaheuristics, 4th International Workshop, {HM} 2007, Dortmund, Germany, October 8-9, 2007, Proceedings},
    volume = {4771},
    year = {2007},
    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-540-75514-2}}
  • [PDF] Thomas Bartz-Beielstein, Wolfgang Konen, and Hartmut Westenberger. Computational Intelligence und Data Mining – Portfoliooptimierung unter Nebenbedingungen. Forschungsbericht, FH Köln, 2007.
    [Bibtex]
    @techreport{Bart07c,
    abstract = {Ziel der Portfoliooptimierung ist die Bestimmung einer optimalen Investmentstrategie an einem Fi- nanzmarkt. Ein Investor muss beispielsweise entscheiden, in welchem Verh{\"a}ltnis verschiedene Anla- gen in einem Depot vorhanden sein sollen. Erfahrungen aus der allt{\"a}glichen Arbeit der Firma Dort- mund Intelligence Project GmbH (DIP) liefern die Grundlage f{\"u}r dieses Forschungsvorhaben.
    Der Auftraggeber ist meistens ein institutioneller oder in manchen F{\"a}llen auch ein verm{\"o}gender priva- ter Anleger. Die Aufgabe besteht nun darin, ein Portfolio zu erstellen, das speziell auf die W{\"u}nsche und Anforderungen des Auftraggebers zugeschnitten ist. Des Weiteren m{\"o}chte der Auftraggeber in m{\"o}glichst kurzen Abst{\"a}nden wissen, ob seine Portfolios durch die Hinzunahme oder durch den Ver- kauf von Anlagen optimiert werden k{\"o}nnen. Die Performance des Portfolios ergibt sich durch die ein- zelnen Anlagen. Da die Optimierung auf historischen Daten basiert, kommt es hier zu einer Diskre- panz zwischen der wirklichen und der berechneten Performance.},
    author = {Bartz-Beielstein, Thomas and Konen, Wolfgang and Westenberger, Hartmut},
    date-added = {2015-11-29T01:38:47GMT},
    date-modified = {2021-07-24 10:11:56 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{Computational Intelligence und Data Mining -- Portfoliooptimierung unter Nebenbedingungen}},
    type = {Forschungsbericht},
    year = {2007},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Wolfgang Konen, and Hartmut Westenberger. Computational Intelligence und Data Mining–-Moderne statistische Verfahren zur experimentellen Versuchsplanung. Forschungsbericht, FH Köln, 2007.
    [Bibtex]
    @techreport{Bart07d,
    abstract = {Methoden der statistischen Versuchsplanung kommen immer dann zum Einsatz, wenn mit m{\"o}glichst geringem Aufwand (Kosten, Zeit) eine verl{\"a}ssliche Aussage {\"u}ber das Verhalten (Leistung) eines Sys- tems zu treffen ist. Unter einem System kann in diesem Zusammenhang ein Objekt mit klar spezifizier- ten Eingabegr{\"o}{\ss}en und messbaren Ausgabegr{\"o}{\ss}en verstanden werden. Durch geschickte Variation der Belegungen der Eingabegr{\"o}{\ss}en (sog. experimentellen Designs) kann der An- wender wichtige Systemeigenschaften und ihre Abh{\"a}ngigkeit von den Eingabegr{\"o}{\ss}en bestimmen.},
    author = {Bartz-Beielstein, Thomas and Konen, Wolfgang and Westenberger, Hartmut},
    date-added = {2015-11-29T01:38:47GMT},
    date-modified = {2021-07-24 10:12:42 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{Computational Intelligence und Data Mining---Moderne statistische Verfahren zur experimentellen Versuchsplanung}},
    type = {Forschungsbericht},
    year = {2007},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Michael Bongards, Christoph Claes, Wolfgang Konen, and Hartmut Westenberger. Datenanalyse und Prozessoptimierung für Kanalnetze und Kläranlagen mit CI-Methoden. In R. Mikut and M. Reischl, editors, Proc. 17th workshop computational intelligence, page 132–138. Universitätsverlag, karlsruhe, 2007.
    [Bibtex]
    @inproceedings{BBKW07a,
    abstract = {Prozesse optimal zu steuern und Prognosen {\"u}ber ihren zuk{\"u}nftigen Verlauf an- zustellen, geh{\"o}rt zu den wichtigsten, aber auch schwierigsten Aufgaben der indu- striellen Praxis. Wir demonstrieren, wie aktuelle Methoden der Computational In- telligence (CI) und des Data Mining ebenso wie klassische Ans{\"a}tze zur Model- lierung, Simulation und Optimierung von Kanalnetzen und Kl{\"a}ranlagen eingesetzt werden k{\"o}nnen. Dabei zeigt sich, dass die Kombination von Standardverfahren aus der Zeitreihenanalyse mit dem Verfahren der sequentiellen Parameteroptimierung schnell zu problemspezifischen Vorhersagemodellen f{\"u}hren kann.},
    author = {Bartz-Beielstein, Thomas and Bongards, Michael and Claes, Christoph and Konen, Wolfgang and Westenberger, Hartmut},
    booktitle = {Proc. 17th Workshop Computational Intelligence},
    date-added = {2015-11-29T01:38:40GMT},
    date-modified = {2017-03-07 09:17:10 +0000},
    editor = {Mikut, R and Reischl, M},
    keywords = {bartzPublic, free},
    pages = {132--138},
    publisher = {Universit{\"a}tsverlag, Karlsruhe},
    rating = {0},
    title = {{Datenanalyse und Prozessoptimierung f{\"u}r Kanalnetze und Kl{\"a}ranlagen mit CI-Methoden}},
    year = {2007},
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  • [DOI] Thomas Bartz-Beielstein and Mike Preuss. Experimental research in evolutionary computation (GECCO 2007). In Proceedings of the 2007 gecco conference companion on genetic and evolutionary computation, page 3001–3020, New York, NY, USA, 2007. Acm.
    [Bibtex]
    @inproceedings{Bart07b,
    abstract = {We present a comprehensive, effective and very efficient methodology for the design and experimental analysis of search heuristics such as evolutionary algorithms, differential evolution, pattern search or even classical methods such as the Nelder-Mead simplex algorithm. Our approach extends the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems. The benefit of combining modern and classical statistical methods is demonstrated. Optimization practitioners receive valuable hints for choosing an adequate heuristic for their optimization problems - theoreticians receive guidelines for testing results systematically on real problem instances. We demonstrate how SPO improves the performance of many search heuristics significantly. However, this performance gain is not available for free. Therefore, costs of this tuning process are discussed. Several examples from theory and practice are used to illustrate typical pitfalls in experimentation. Software tools implementing procedures described in this tutorial are freely available.},
    address = {New York, NY, USA},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation},
    date-added = {2015-11-29T01:39:06GMT},
    date-modified = {2017-03-07 21:25:55 +0000},
    doi = {10.1145/1274000.1274102},
    isbn = {978-1-59593-698-1},
    keywords = {bartzPublic, BartzTutorial, nonfree},
    pages = {3001--3020},
    publisher = {ACM},
    rating = {0},
    title = {{Experimental research in evolutionary computation (GECCO 2007)}},
    url = {http://doi.acm.org/10.1145/1274000.1274102},
    year = {2007},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://doi.acm.org/10.1145/1274000.1274102},
    bdsk-url-2 = {http://dx.doi.org/10.1145/1274000.1274102}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Daniel Blum, and Jürgen Branke. Particle swarm optimization and sequential sampling in noisy environments, page 261–273. Springer us, Boston, MA, 2007.
    [Bibtex]
    @inbook{BBB07,
    abstract = {For many practical optimization problems, the evaluation of a solution is subject to noise, and optimization heuristics capable of handling such noise are needed. In this paper we examine the influence of noise on particle swarm optimization and demonstrate that the resulting stagnation can not be removed by parameter optimization alone, but requires a reduction of noise through averaging over multiple samples. In order to reduce the number of required samples, we propose a combination of particle swarm optimization and a statistical sequential selection procedure, called optimal computing budget allocation, which attempts to distribute a given number of samples in the most effective way. Experimental results show that this new algorithm indeed outperforms the other alternatives.},
    address = {Boston, MA},
    author = {Bartz-Beielstein, Thomas and Blum, Daniel and Branke, J{\"u}rgen},
    booktitle = {Metaheuristics: Progress in Complex Systems Optimization},
    date-added = {2021-07-23 22:57:23 +0200},
    date-modified = {2021-07-23 22:58:33 +0200},
    doi = {10.1007/978-0-387-71921-4_14},
    editor = {Doerner, Karl F. and Gendreau, Michel and Greistorfer, Peter and Gutjahr, Walter and Hartl, Richard F. and Reimann, Marc},
    isbn = {978-0-387-71921-4},
    keywords = {bartzPublic},
    pages = {261--273},
    publisher = {Springer US},
    title = {Particle Swarm Optimization and Sequential Sampling in Noisy Environments},
    url = {https://doi.org/10.1007/978-0-387-71921-4_14},
    year = {2007},
    bdsk-file-1 = {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},
    bdsk-url-1 = {https://doi.org/10.1007/978-0-387-71921-4_14}}
  • [PDF] Wolfgang Konen, Thomas Bartz-Beielstein, and Hartmut Westenberger. Computational Intelligence und Data Mining – Datenanalyse und Prozessoptimierung am Beispiel Kläranlagen. Forschungsbericht, FH Köln, 2007.
    [Bibtex]
    @techreport{Kone07a,
    abstract = {Die zunehmende Vernetzung industrieller und wirtschaftlicher Anlagen sowie vermehrt auf- tretende automatische Datenerhebungen ergeben die M{\"o}glichkeiten, aber auch die Last, immer detailliertere Datenmengen zu analysieren. Dies geschieht oftmals vor dem Hinter- grund, Prozesse optimal zu steuern oder Prognosen {\"u}ber den zuk{\"u}nftigen Verlauf anzustel- len.
    Ziel des Projektes ist es, sowohl aktuelle bis aktuellste Methoden zur Modellierung, Simulati- on und Optimierung komplexer Prozesse einzusetzen. Hierzu werden praxisbew{\"a}hrte Me- thoden der Computational Intelligence (CI) und des Data Mining am Institut f{\"u}r Informatik der FH K{\"o}ln geb{\"u}ndelt zum Einsatz gebracht. In Kooperationsprojekten mit Partnern aus Indust- rie und Wirtschaft werden die Methoden auf Einsetzbarkeit und Leistungsf{\"a}higkeit gepr{\"u}ft. Der Einsatz in diesen konkreten Anwendungsf{\"a}llen erm{\"o}glicht es, die Reichweite und die Grenzen verschiedener, oftmals komplexer CI und Data Mining Methoden auch f{\"u}r Praktiker aus Industrie und Wirtschaft gut fassbar darzustellen. Das Institut f{\"u}r Informatik der FH K{\"o}ln unterst{\"u}tzt Unternehmen beim Einsatz dieser Methoden.},
    author = {Konen, Wolfgang and Bartz-Beielstein, Thomas and Westenberger, Hartmut},
    date-added = {2015-11-29T01:41:06GMT},
    date-modified = {2021-07-24 10:11:13 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{Computational Intelligence und Data Mining -- Datenanalyse und Prozessoptimierung am Beispiel Kl{\"a}ranlagen}},
    type = {Forschungsbericht},
    year = {2007},
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  • [PDF] Jörn Mehnen, Thomas Michelitsch, Christian Lasarczyk, and Thomas Bartz-Beielstein. Multi-objective evolutionary design of mold temperature control using DACE for parameter optimization. International journal of applied electromagnetics and mechanics, 25(1–4):661–667, 2007.
    [Bibtex]
    @article{MMLB07a,
    abstract = {The design of mold temperature control strategies (MTCS) is a challenging multi- objective optimization task which demands for advanced optimization methods. Evolutionary algorithms (EA) are powerful stochastically driven search techniques. In this paper an EA is applied to a multi-objective problem using aggregation. The performance of the evolutionary search can be improved using systematic parameter adaptation. The DACE technique (design and analysis of computer experiments) is used to find good MOEA (multi-objective evolutionary algorithm) parameter settings to get improved solutions of the MTCS problem. SPO (sequential parameter optimization), i.e., an automatic and integrated approach, which extends DACE, is applied to find the statistically significant and most promising EA parameters.},
    author = {Mehnen, J{\"o}rn and Michelitsch, Thomas and Lasarczyk, Christian and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:41:52GMT},
    date-modified = {2017-03-07 22:38:49 +0000},
    journal = {International Journal of Applied Electromagnetics and Mechanics},
    keywords = {bartzPublic, nonfree},
    number = {1--4},
    pages = {661--667},
    rating = {0},
    title = {{Multi-objective evolutionary design of mold temperature control using DACE for parameter optimization}},
    url = {http://iospress.metapress.com/content/751K5GG10P79Q501},
    volume = {25},
    year = {2007},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://iospress.metapress.com/content/751K5GG10P79Q501}}
  • [PDF] Mike Preuss and Thomas Bartz-Beielstein. Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms. In Fernando Lobo, Claudio Lima, and Zbigniew Michalewicz, editors, Parameter setting in evolutionary algorithms, page 91–120. Springer, Berlin, Heidelberg, New York, 2007.
    [Bibtex]
    @incollection{Preu07a,
    address = {Berlin, Heidelberg, New York},
    author = {Preuss, Mike and Bartz-Beielstein, Thomas},
    booktitle = {Parameter Setting in Evolutionary Algorithms},
    date-added = {2015-11-29T01:42:47GMT},
    date-modified = {2017-01-14 15:29:30 +0000},
    editor = {Lobo, Fernando and Lima, Claudio and Michalewicz, Zbigniew},
    keywords = {bartzPublic, nonfree},
    pages = {91--120},
    publisher = {Springer},
    rating = {0},
    title = {{Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms}},
    year = {2007},
    bdsk-file-1 = {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}}
  • [PDF] Hartmut Westenberger, Wolfgang Konen, and Thomas Bartz-Beielstein. Computational Intelligence und Data Mining – Business Intelligence an Hochschulen. Forschungsbericht, FH Köln, 2007.
    [Bibtex]
    @techreport{West07a,
    abstract = {Hochschulen werden in der zunehmenden Konkurrenzsituation von Bildungstr{\"a}gern nur dann bestehen, beziehungsweise einen befriedigenden Platz in der Hochschullandschaft einnehmen, wenn sie ihre Leistungsziele klar definieren und die entsprechenden Leistungsprozesse mittels geeigneter Ma{\ss}nahmen zielorientiert optimieren. Der Gesch{\"a}ftserfolg von Hochschulen wird typischerweise mit Schl{\"u}sselkennzahlen wie Absolventenzahl pro Lehrkraft, Abbrecherquote, mittlere Studiendauer, Drittmitteleinnahmen pro Lehrkraft, Anzahl von Ver{\"o}ffentlichungen etc. gemessen.
    Entsprechende statistische Kennziffern werden bereits heute aus den operativen Datenquellen der Hochschulverwaltungssysteme abgeleitet. Business Intelligence (BI) geht aber einen Schritt weiter und erhebt den Anspruch, alle erforderlichen Datenquellen verf{\"u}gbar zu machen und systematisch auszuwerten, die das Verst{\"a}ndnis in die Wirkungszusammenh{\"a}nge von Ma{\ss}nahmen und Auswirkungen erweitern helfen. Damit stellt BI eine optimierte Grundlage f{\"u}r effektive Entscheidungsprozesse bereit.
    },
    author = {Westenberger, Hartmut and Konen, Wolfgang and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:43:38GMT},
    date-modified = {2021-07-24 10:10:33 +0200},
    institution = {FH K{\"o}ln},
    keywords = {bartzPublic, free},
    publisher = {FH K{\"o}ln},
    rating = {0},
    title = {{Computational Intelligence und Data Mining -- Business Intelligence an Hochschulen}},
    type = {Forschungsbericht},
    year = {2007},
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2006

  • [DOI] Sandor Markon, Hajime Kita, Hiroshi Kise, and Thomas Bartz-Beielstein, editors. Control of Traffic Systems in Buildings. Springer, Berlin, Heidelberg, New York, 2006.
    [Bibtex]
    @book{Mark06a,
    address = {Berlin, Heidelberg, New York},
    date-added = {2015-11-29T01:45:11GMT},
    date-modified = {2017-03-05 00:29:08 +0000},
    doi = {10.1007/1-84628-449-X},
    editor = {Markon, Sandor and Kita, Hajime and Kise, Hiroshi and Bartz-Beielstein, Thomas},
    keywords = {bartzPublic, nonfree},
    publisher = {Springer},
    rating = {0},
    title = {{Control of Traffic Systems in Buildings}},
    url = {http://www.springer.com/de/book/9781846284489#otherversion=9781849966047},
    year = {2006},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://www.springer.com/de/book/9781846284489#otherversion=9781849966047},
    bdsk-url-2 = {http://dx.doi.org/10.1007/1-84628-449-X}}
  • [PDF] Thomas Bartz-Beielstein, Gundel Jankord, Boris Naujoks, and others, editors. Hans–Paul Schwefel–-Festschrift. Dortmund university, chair of systems analysis, Dortmund, Germany, 2006.
    [Bibtex]
    @book{Bart06b,
    address = {Dortmund, Germany},
    date-added = {2015-11-29T01:45:10GMT},
    date-modified = {2017-01-14 14:43:23 +0000},
    editor = {Bartz-Beielstein, Thomas and Jankord, Gundel and Naujoks, Boris and others},
    keywords = {bartzPublic, free},
    publisher = {Dortmund University, Chair of Systems Analysis},
    rating = {0},
    title = {{Hans--Paul Schwefel---Festschrift}},
    year = {2006},
    bdsk-file-1 = {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}}
  • [PDF] Bastian Baranski, Thomas Bartz-Beielstein, Rüdiger Ehlers, Thusithan Kajendran, Björn Kosslers, Jörn Mehnen, Tomasz Polazek, Ralf Reimholz, Jens Schmidt, Karlheinz Schmitt, Danny Seis, Rafael Slodzinski, Simon Steeg, Nils Wiemann, and Marc Zimmermann. Advanced strategy representations for the iterated prisoner’s dilemma. In Proceedings euro xxi conference 2006. 21st european conference on operational research, 2006.
    [Bibtex]
    @inproceedings{Bara06a,
    abstract = {Although many studies have been conducted on evolving strategies for the iterated prisoners dilemma (IPD) using evolutionary algorithms,
    most of them use rather simple representations like look-up tables. Knowledge about the evolu- tion of strategies with complex representations remains limited.
    Complex representations may lead to new strategies and may be closer to the human decision making process (thinking). Our main question is:
    Do complex represantations have substantial advantages over simpler ones in context of the IPD?
    In this paper we introduce two novel advanced representations: an exhaustive approach based on linear genetic programming and an evolution strategy based approach which reflects a psychological model.
    We show that both approaches lead to the generation of usable tournament strategies. We also use a cooevolutionary environment to study the evolution of cooperation, leading to diverse results.
    Additionally, the application of decision tree techniques to generate a strategy training set from real world tournaments is discussed. Strategies evolved with such training sets may perform well in subsequent real world tournaments, as other participants tend to imitate successful strategies of previuos tournaments.},
    annote = {(CD--ROM)},
    author = {Baranski, Bastian and Bartz-Beielstein, Thomas and Ehlers, R{\"u}diger and Kajendran, Thusithan and Kosslers, Bj{\"o}rn and Mehnen, J{\"o}rn and Polazek, Tomasz and Reimholz, Ralf and Schmidt, Jens and Schmitt, Karlheinz and Seis, Danny and Slodzinski, Rafael and Steeg, Simon and Wiemann, Nils and Zimmermann, Marc},
    booktitle = {Proceedings EURO XXI Conference 2006. 21st European Conference on Operational Research},
    date-added = {2015-11-29T01:38:23GMT},
    date-modified = {2017-03-06 22:25:23 +0000},
    keywords = {bartzPublic, free},
    rating = {0},
    title = {{Advanced strategy representations for the iterated prisoner's dilemma}},
    year = {2006},
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  • [PDF] [DOI] Bastian Baranski, Thomas Bartz-Beielstein, Rüdiger Ehlers, Thusithan Kajendran, Björn Kosslers, Jörn Mehnen, Tomasz Polazek, Ralf Reimholz, Jens Schmidt, Karlheinz Schmitt, Danny Seis, Rafael Slodzinski, Simon Steeg, Nils Wiemann, and Marc Zimmermann. High-order punishment and the evolution of cooperation. In Hans-Georg Beyer and others, editors, Proc. genetic and evolutionary computation conf. (gecco 2006), seattle wa, page 379–380, New York, 2006. Acm press.
    [Bibtex]
    @inproceedings{Bara06c,
    address = {New York},
    author = {Baranski, Bastian and Bartz-Beielstein, Thomas and Ehlers, R{\"u}diger and Kajendran, Thusithan and Kosslers, Bj{\"o}rn and Mehnen, J{\"o}rn and Polazek, Tomasz and Reimholz, Ralf and Schmidt, Jens and Schmitt, Karlheinz and Seis, Danny and Slodzinski, Rafael and Steeg, Simon and Wiemann, Nils and Zimmermann, Marc},
    booktitle = {Proc. Genetic and Evolutionary Computation Conf. (GECCO 2006), Seattle WA},
    date-added = {2015-11-29T01:38:19GMT},
    date-modified = {2021-07-24 10:09:12 +0200},
    doi = {10.1145/1143997.1144065},
    editor = {Beyer, Hans-Georg and others},
    keywords = {bartzPublic},
    pages = {379--380},
    publisher = {ACM Press},
    rating = {0},
    title = {{High-order punishment and the evolution of cooperation}},
    year = {2006},
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    bdsk-url-1 = {https://doi.org/10.1145/1143997.1144065}}
  • [PDF] [DOI] Bastian Baranski, Thomas Bartz-Beielstein, Rüdiger Ehlers, Thusithan Kajendran, Björn Kosslers, Jörn Mehnen, Tomasz Polazek, Ralf Reimholz, Jens Schmidt, Karlheinz Schmitt, Danny Seis, Rafael Slodzinski, Simon Steeg, Nils Wiemann, and Marc Zimmermann. The impact of group reputation in multiagent environments. In D. B. et al Fogel, editor, Proc. 2006 congress on evolutionary computation (cec’06) within fourth ieee world congress on computational intelligence (wcci’06), vancouver bc, page 1224–1231, Piscataway NJ, 2006. Ieee press.
    [Bibtex]
    @inproceedings{Bara06b,
    abstract = {This paper presents results from extensive simulation studies on the iterated prisoner's dilemma. Two models were imple- mented: a nongroup model in order to study fundamental principles of cooperation and a model to imitate ethnocentrism. Some extensions of Axelrod's elementary model implemented individual reputation. We furthermore introduced group reputation to provide a more realistic scenario. In an environment with group reputation the behavior of one agent will affect the reputation of the whole group and vice-versa. While kind agents (e. g. those with a cooperative behavior) lose reputation when being in a group, in which defective strategies are more common, agents with defective behavior on the other hand benef t from a group with more cooperative strategies. We demonstrate that group reputation decreases cooperation with the in-group and increases cooperation with the out-group.
    },
    address = {Piscataway NJ},
    author = {Baranski, Bastian and Bartz-Beielstein, Thomas and Ehlers, R{\"u}diger and Kajendran, Thusithan and Kosslers, Bj{\"o}rn and Mehnen, J{\"o}rn and Polazek, Tomasz and Reimholz, Ralf and Schmidt, Jens and Schmitt, Karlheinz and Seis, Danny and Slodzinski, Rafael and Steeg, Simon and Wiemann, Nils and Zimmermann, Marc},
    booktitle = {Proc. 2006 Congress on Evolutionary Computation (CEC'06) within Fourth IEEE World Congress on Computational Intelligence (WCCI'06), Vancouver BC},
    date-added = {2015-11-29T01:38:24GMT},
    date-modified = {2017-03-08 23:02:12 +0000},
    doi = {10.1109/CEC.2006.1688449},
    editor = {Fogel, D B et al},
    isbn = {0-7803-9487-9},
    keywords = {bartzPublic},
    pages = {1224--1231},
    publisher = {IEEE Press},
    rating = {0},
    read = {Yes},
    title = {{The impact of group reputation in multiagent environments}},
    url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1688449},
    year = {2006},
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    bdsk-url-2 = {http://dx.doi.org/10.1109/CEC.2006.1688449}}
  • [PDF] Thomas Bartz-Beielstein and Mike Preuss. Considerations of Budget Allocation for Sequential Parameter Optimization (SPO). In L. Paquete and others, editors, Workshop on empirical methods for the analysis of algorithms, proceedings, page 35–40, Reykjavik, Iceland, 2006.
    [Bibtex]
    @inproceedings{Bart06j,
    abstract = {Obviously, it is not a good idea to apply an optimization algorithm with wrongly speci ed parameter settings, a situation which can be avoided by applying algorithm tuning. Sequential tuning procedures are considered more e cient than single-stage procedures. [1] introduced a sequential approach for algorithm tuning that has been successfully applied to several real-world optimization tasks and experimental studies. The sequential procedure requires the speci cation of an initial sample size k. Small k values lead to poor models and thus poor predictions for the subsequent stages, whereas large values prevent an extensive search and local ne tuning. This study analyzes the interaction between global and local search in sequential tuning procedures and gives recommendations for an adequate budget allocation. Furthermore, the integration of hypothesis testing for increasing e ectiveness of the latter phase is investigated.
    },
    address = {Reykjavik, Iceland},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Workshop on Empirical Methods for the Analysis of Algorithms, Proceedings},
    date-added = {2015-11-29T01:39:07GMT},
    date-modified = {2017-03-07 09:14:37 +0000},
    editor = {Paquete, L and others},
    keywords = {bartzPublic, free},
    pages = {35--40},
    rating = {0},
    title = {{Considerations of Budget Allocation for Sequential Parameter Optimization (SPO)}},
    url = {http://www.imada.sdu.dk/~marco/EMAA/Papers/EMAA06-bartz.pdf},
    year = {2006},
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    bdsk-url-1 = {http://www.imada.sdu.dk/~marco/EMAA/Papers/EMAA06-bartz.pdf}}
  • [DOI] Thomas Bartz-Beielstein. Experimental Research in Evolutionary Computation–-The New Experimentalism. Natural Computing Series. Springer, Berlin, Heidelberg, New York, 2006.
    [Bibtex]
    @book{Bart06a,
    abstract = {Rigorously proven upper and lower run-time bounds for simplified evolutionary algorithms on artificial optimization problems on the one hand and endless tables of benchmark results for real-world algorithms on today's or yesterday's hardware on the other, is that all one can do to justify their invention, existence, or even spreading use? Thomas Bartz-Beielstein gives thoughtful answers to such questions that have bothered him since he joined the team of researchers at the Chair of Systems Analysis within the Department of Com- puter Science at the University of Dortmund. He brings together recent results from statistics, epistemology of experimentation, and evolutionary computation. After a long period in which experimentation has been discredited in evo- lutionary computation, it is regaining importance. This book far exceeds a discussion of often-met points of criticism of the usual experimental approach like missing standards, different measures, and inaccurate and irreproducible results. Also, fundamental objections against the experimental approach are discussed and cleared up. This work shows ways and means to close the gap between theoretical and experimental approaches in algorithm engineering. It becomes clear that statistical tests are the beginning and not the end of experimental analyses. Vital in this context is the differentiation between statistically relevant and scientifically meaningful results, which is clearly developed by Thomas Bartz-Beielstein.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:42GMT},
    date-modified = {2019-08-06 21:50:42 +0200},
    doi = {10.1007/3-540-32027-X},
    groups = {bartzPublic},
    isbn = {3-540-32026-1},
    keywords = {bartzPublic, nonfree, Bart19g},
    publisher = {Springer},
    rating = {0},
    series = {Natural Computing Series},
    title = {{Experimental Research in Evolutionary Computation---The New Experimentalism}},
    url = {http://dx.doi.org/10.1007/3-540-32027-X},
    year = {2006},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/3-540-32027-X}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Mike Preuss, and Günter Rudolph. Investigation of One-Go Evolution Strategy/Quasi-Newton Hybridizations. In Blesa Aguilera and others, editors, Proceedings third international workshop hybrid metaheuristics (hm’06), page 178–191, Berlin, Heidelberg, New York, 2006. Springer.
    [Bibtex]
    @inproceedings{Bart06h,
    abstract = {It is general knowledge that hybrid approaches can improve the performance of search heurististics. The first phase, exploration, should detect regions of good solutions, whereas the second phase, ex- ploitation, shall tune these solutions locally. Therefore a combination (hybridization) of global and local optimization techniques is recom- mended. Although plausible at the first sight, it remains unclear how to implement the hybridization, e.g., to distribute the resources, i.e., num- ber of function evaluations or CPU time, to the global and local search optimization algorithm. This budget allocation becomes important if the available resources are very limited. We present an approach to analyze hybridization in this case. An evolution strategy and a quasi-Newton method are combined and tested on standard test functions.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Rudolph, G{\"u}nter},
    booktitle = {Proceedings Third International Workshop Hybrid Metaheuristics (HM'06)},
    date-added = {2015-11-29T01:39:07GMT},
    date-modified = {2017-03-07 21:56:41 +0000},
    doi = {10.1007/11890584_14},
    editor = {Aguilera, Blesa and others},
    isbn = {978-3-540-46384-9},
    keywords = {bartzPublic},
    pages = {178--191},
    publisher = {Springer},
    rating = {0},
    title = {{Investigation of One-Go Evolution Strategy/Quasi-Newton Hybridizations}},
    url = {http://dx.doi.org/10.1007/11890584_14},
    year = {2006},
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    bdsk-url-1 = {http://dx.doi.org/10.1007/11890584_14}}
  • [PDF] Thomas Bartz-Beielstein and Mike Preuss. Moderne Methoden zur experimentellen Analyse evolutionärer Verfahren. In R. Mikut and M. Reischl, editors, Proc. 16th workshop computational intelligence, page 25–32. Universitätsverlag, karlsruhe, 2006.
    [Bibtex]
    @inproceedings{Bart06i,
    abstract = {Die Beschreibung einer Methodik zur Analyse der Industrietauglichkeit evolu- tion{\"a}rer Algorithmen steht im Mittelpunkt dieses Beitrags. Dazu werden moderne statistische Verfahren wie ``Design and Analysis of Computer Experiments'' mit klas- sischen Verfahren der experimentellen Versuchsplanung (Design of Experiments) kombiniert. Diese Methoden eignen sich sehr gut, um den experimentellen Ver- gleich verschiedener Algorithmen zu objektivieren und praxisnah durchzuf{\"u}hren. Sie wurden bereits f{\"u}r unterschiedliche Simulations- und Optimierungsszenarien einge- setzt, z.B. f{\"u}r die Optimierung von Transportproblemen in Geb{\"a}uden (Fahrstuhl- steuerungsprobleme). Aus diesen Szenarien lassen sich allgemeine und zugleich pra- xisrelevante Tests definieren, die die Grundlage f{\"u}r den Vergleich von CI-Methoden (Fuzzy-Systeme, neuronale Netze und evolution{\"a}re Algorithmen) mit klassischen Verfahren der Optimierung bilden k{\"o}nnen.
    Des Weiteren stellen wir eine frei verf{\"u}gbare Sammlung von Programmen zur se- quenziellen Analyse von Optimieralgorithmen zur Verf{\"u}gung (SPOT: Sequential Pa- rameter Optimization Toolbox), die f{\"u}r die oben genannten Zwecke entwickelt wird. Sie beinhaltet klar definierte Schnittstellen, so dass g{\"a}ngige Optimierungstools mit geringem Aufwand eingebunden und analysiert werden k{\"o}nnen. Die Analysem{\"o}g- lichkeiten gehen {\"u}ber klassische Vergleiche (Bericht von Mittelwerten, Standardab- weichungen, etc.) weit hinaus und erm{\"o}glichen ein tiefer gehendes Verst{\"a}ndnis der Funktionsweise der Algorithmen.},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    booktitle = {Proc. 16th Workshop Computational Intelligence},
    date-added = {2015-11-29T01:39:07GMT},
    date-modified = {2017-03-07 22:15:25 +0000},
    editor = {Mikut, R and Reischl, M},
    keywords = {bartzPublic, free},
    pages = {25--32},
    publisher = {Universit{\"a}tsverlag, Karlsruhe},
    rating = {0},
    title = {{Moderne Methoden zur experimentellen Analyse evolution{\"a}rer Verfahren}},
    year = {2006},
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  • [PDF] Thomas Bartz-Beielstein. Neyman-Pearson Theory of Testing and Mayo’s Extensions in Evolutionary Computing (preprint). Sonderforschungsbereich (SFB) 531, Universität Dortmund, 2006.
    [Bibtex]
    @techreport{Bart06g,
    abstract = {Evolutionary computation (EC) is a relatively new discipline in computer science (Eiben & Smith, 2003). It tackles hard real-world optimization problems, e.g., problems from chemical engineering, airfoil optimization, or bioinformatics, where classical methods from mathematical optimization fail. Many theoretical results in this field are too abstract, they do not match with reality. To develop problem specific algorithms, experimentation is necessary. During the first phase of experimental research in EC (before 1980), which can be characterized as foundation and development, the comparison of different algorithms was mostly based on mean values, nearly no further statistics have been used. In the second phase, where EC moved to mainstream (1980 - 2000), classical statistical methods were introduced.
    There is a strong need to compare EC algorithms to mathematical optimization (main stream) methods. Adequate statistical tools for EC are developed in the third phase (since 2000). They should be able to cope with problems like small sample sizes, nonnormal distributions, noisy results, etc. However---even if these tools are under development---they do not bridge the gap between the statistical significance of an experimental result and its scientific meaning. Based on Mayo's learning model (NPT) we will propose some ideas how to bridge this gap (Mayo, 1983, 1996). We will present plots of the observed significance level and discuss the sequential parameter optimization (SPO) approach. SPO is a heuristic, but implementable approach, which provides a framework for a sound statistical methodology in EC (Bartz-Beielstein, 2006).},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:28GMT},
    date-modified = {2021-07-24 10:07:33 +0200},
    institution = {Universit{\"a}t Dortmund},
    keywords = {bartzPublic, free},
    rating = {0},
    title = {{Neyman-Pearson Theory of Testing and Mayo's Extensions in Evolutionary Computing} (preprint)},
    type = {Sonderforschungsbereich (SFB) 531},
    year = {2006},
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    bdsk-url-1 = {http://www.error06.econ.vt.edu/bartzerror2006.pdf}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Annette Chmielewski, Michael Janas, Boris Naujoks, and Robert Scheffermann. Optimizing door assignment in LTL-terminals by evolutionary multiobjective algorithms. In D. B. et al Fogel, editor, Proc. 2006 congress on evolutionary computation (cec’06) within fourth ieee world congress on computational intelligence (wcci’06), vancouver bc, page 348–354, Piscataway NJ, 2006. Ieee press.
    [Bibtex]
    @inproceedings{Bart06e,
    abstract = { In less-than-truckload terminals arriving trucks have to be allocated to a gate and to a time slot for unloading. The allocation to a specific gate results in different transporta- tion volumes for the forklift trucks inside of the terminal, depending on the destinations of the truck's loads. While minimizing these transports the time for trucks waiting to be ordered to a gate also has to be minimized. For the first time this problem has been tackled as a 2-objective optimization problem and was solved by an (1+1)-evolution strategy. We developed a model which is derived from real freight forwarder's data and represents a small company's terminal on an average workday.
    },
    address = {Piscataway NJ},
    author = {Bartz-Beielstein, Thomas and Chmielewski, Annette and Janas, Michael and Naujoks, Boris and Scheffermann, Robert},
    booktitle = {Proc. 2006 Congress on Evolutionary Computation (CEC'06) within Fourth IEEE World Congress on Computational Intelligence (WCCI'06), Vancouver BC},
    date-added = {2015-11-29T01:38:39GMT},
    date-modified = {2017-03-07 22:55:42 +0000},
    doi = {10.1109/CEC.2006.1688288},
    editor = {Fogel, D B et al},
    isbn = {0-7803-9487-9},
    keywords = {bartzPublic},
    pages = {348--354},
    publisher = {IEEE Press},
    rating = {0},
    title = {{Optimizing door assignment in LTL-terminals by evolutionary multiobjective algorithms}},
    url = {http://dx.doi.org/10.1109/CEC.2006.1688288},
    year = {2006},
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  • [PDF] Thomas Bartz-Beielstein. SPOT–-A Toolbox for Visionary Ideas. In Thomas Bartz-Beielstein, Gundel Jankord, Boris Naujoks, and others, editors, Hans–paul schwefel–-festschrift, page 21–26. Dortmund university, chair of systems analysis, Dortmund, Germany, 2006.
    [Bibtex]
    @incollection{Bart06c,
    address = {Dortmund, Germany},
    author = {Bartz-Beielstein, Thomas},
    booktitle = {Hans--Paul Schwefel---Festschrift},
    date-added = {2015-11-29T01:38:23GMT},
    date-modified = {2017-10-26 09:42:08 +0000},
    editor = {Bartz-Beielstein, Thomas and Jankord, Gundel and Naujoks, Boris and others},
    keywords = {bartzPublic, free},
    pages = {21--26},
    publisher = {Dortmund University, Chair of Systems Analysis},
    rating = {0},
    title = {{SPOT---A Toolbox for Visionary Ideas}},
    year = {2006},
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  • [PDF] Boris Naujoks, Domenico Quagliarella, and Thomas Bartz-Beielstein. Sequential parameter optimisation of evolutionary algorithms for airfoil design. In G. Winter and others, editors, Proc. design and optimization: methods and applications, (ercoftac’06), page 231–235. University of las palmas de gran canaria, 2006.
    [Bibtex]
    @inproceedings{Nauj06a,
    abstract = {More and more complex optimisation techniques play an increasing role in todays industry. Different techniques like gradient based methods or evolutionary search techniques are cou- pled (hybridisation, memetic algorithms [1]), enhanced by methods to fasten objective func- tion evaluations (fitness approximation, metamodel assisted optimisation[2, 3]), or applied to more complex tasks with more than one objective function (multi-objective optimisation [4, 5]). Each of these enhanced techniques is able to improve optimisation results. Utilising not only one of them promises to further meliorate results, what is pushed by industrial needs.
    The drawback of such highly sophisticated methods and techniques is the growing number of parameters. Due to possible complex interactions, these parameters must by handled with care. A wrong parameter setting may lead to unwanted and bad optimisation results while the right parameter setting for the same algorithm-application combination may lead to ex- tremely good results. This means, that the setting of parameters plays a major role in design optimisation.
    This article describes the sequential parameter optimization (SPO) framework [6, 7]. SPO has been succesfully applied to optimisation problems in the following domains: Machine engi- neering, Aerospace industry, Elevator group control, Algorithm engineering, Graph drawing, Algorithmic chemistry, Technical Thermodynamics, Agri-environmental policy-switchings, vehicle routing, and bioinformatics.},
    author = {Naujoks, Boris and Quagliarella, Domenico and Bartz-Beielstein, Thomas},
    booktitle = {Proc. Design and Optimization: Methods and Applications, (ERCOFTAC'06)},
    date-added = {2015-11-29T01:42:11GMT},
    date-modified = {2017-03-07 23:36:55 +0000},
    editor = {Winter, G and others},
    keywords = {bartzPublic, free},
    pages = {231--235},
    publisher = {University of Las Palmas de Gran Canaria},
    rating = {0},
    title = {{Sequential parameter optimisation of evolutionary algorithms for airfoil design}},
    year = {2006},
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2005

  • [PDF] Thomas Bartz-Beielstein. Evolution Strategies and Threshold Selection. In M. J. Blesa Aguilera, C. Blum, A. Roli, and M. Sampels, editors, Proceedings second international workshop hybrid metaheuristics (hm’05), page 104–115, Berlin, Heidelberg, New York, 2005. Springer.
    [Bibtex]
    @inproceedings{Bart05b,
    abstract = {A hybrid approach that combines the( 1+1)-ES and thresh- old selection methods is developed. The framework of the new experimentalism is used to perform a detailed statistical analysis of the effects that are caused by this hybridization. Experimental results on the sphere function indicate that hybridization worsens the performance of the evolution strategy, because evolution strategies are well-scaled hill- climbers: the additional threshold disturbs the self-adaptation process of the evolution strategy. Theory predicts that the hybrid approach might be advantageous in the presence of noise. This effect could be observed--- however, a proper fine tuning of the algorithm's parameters appears to be advantageous.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas},
    booktitle = {Proceedings Second International Workshop Hybrid Metaheuristics (HM'05)},
    date-added = {2015-11-29T01:38:24GMT},
    date-modified = {2017-03-07 20:58:52 +0000},
    editor = {Blesa Aguilera, M J and Blum, C and Roli, A and Sampels, M},
    keywords = {bartzPublic},
    pages = {104--115},
    publisher = {Springer},
    rating = {0},
    read = {Yes},
    title = {{Evolution Strategies and Threshold Selection}},
    year = {2005},
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  • [PDF] [DOI] Thomas Bartz-Beielstein. New Experimentalism Applied to Evolutionary Computation. PhD thesis, Universität Dortmund, Germany, apr 2005.
    [Bibtex]
    @phdthesis{Bart05a,
    abstract = {This thesis develops a solid statistical methodology to analyze search heuristics such as evolutionary algorithm
    s based on the concept of the new experimentalism. The new experimentalism is an influential discipline in the modern philosophy of science. The new experimentalists are seeking
    for a relatively secure basis for science, not in theory or observation, but in experiment. Deborah Mayo - one of its prominent proponents - developed a detailed way in which sc
    ientific claims can be validated by experiment. First, the concept of the new experimentalism for computer experiments is introduced. The difference between significant and meani
    ngful results is detailed. Following Mayo, a re-interpretation of the Neyman-Pearson theory of testing for computer experiments is given. Since statistical tests can be used as l
    earning tools, they provide means to extend widely accepted popperian paradigms. Models are characterized as central elements of science. We claim that experiment dominates theor
    y. Many, even conflicting, theories can co-exist independently for one unique experimental result. Maybe there is no theory applying to every phenomenon, but many simple theories
    describing what happens from case to case. Basic definitions from computational statistics, classical design of experiments (DOE), and modern design of computer experiments (DAC
    E) are explained to provide the reader with the required background information from statistics. An elevator group control model, which has been developed in cooperation with one
    of the world's leading elevator manufacturers, is introduced as an example for complex real-world optimization problems. It is used to illustrate the difference between art
    ificial functions from test suites and real-world problems. Problems related to these commonly used test-suites are discussed. Experimenters have to decide where to place sample
    points. Classical and modern experimental designs are compared to describe the difference between space-filling designs and designs that place experimental points at the boundari
    es of the experimental region. In many situations, it might be beneficial to generate the design points not at once, but sequentially. A sequential design, which provides a basis
    for a parameter tuning method, is developed. Exogenous strategy parameters, which have to be specified before an optimization algorithm can be started, are presented for determi
    nistic and stochastic search algorithms. The discussion of the concept of optimization provides the foundation to define performance measures for search heuristics. Optimization
    relies on a number of very restrictive assumptions that are not met in many real-world settings. Efficiency and effectivity are introduced with respect to these problems as two i
    mportant categories to classify performance measures. As the pre-requisites have been introduced, experiments can be performed and analyzed in framework of the new experimentalis
    m. A classical approach, based on DOE, is presented first. Then, sequential parameter optimization (SPO) is developed as a modern methodology to improve ('tune') and co
    mpare the performance of algorithms. It is demonstrated how the tuning process, which requires only a relatively small number of experiments, can improve the algorithm's per
    formance significantly. Even more, the new experimentalism, as introduced and applied in this thesis, provides means to understand the algorithm's performance. Various schem
    es for selection under noise are introduced to demonstrate this feature. To give an example, it is demonstrated how threshold selection can improve the local and global performan
    ce of search heuristics under noise. Threshold selection can be characterized as a smart and simple heuristic that performs relatively good in certain environments. These heurist
    ics are interpreted in Herbert Simon's framework of bounded rationality. Finally, a commonly accepted model that describes the relation between experiment and theory is revised and enhanced.},
    author = {Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:38:39GMT},
    date-modified = {2017-03-07 22:42:37 +0000},
    doi = {10.17877/DE290R-15667},
    keywords = {bartzPublic, free},
    month = apr,
    publisher = {Universit{\"a}t Dortmund, Germany},
    rating = {0},
    school = {Universit{\"a}t Dortmund, Germany},
    title = {{New Experimentalism Applied to Evolutionary Computation}},
    url = {http://hdl.handle.net/2003/21461},
    year = {2005},
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    bdsk-url-1 = {http://hdl.handle.net/2003/21461},
    bdsk-url-2 = {http://dx.doi.org/10.17877/DE290R-15667}}
  • [PDF] Thomas Bartz-Beielstein, Daniel Blum, and Jürgen Branke. Particle Swarm Optimization and Sequential Sampling in Noisy Environments. In Richard Hartl and Karl Doerner, editors, Proceedings 6th metaheuristics international conference (mic2005), page 89–94, Vienna, Austria, 2005.
    [Bibtex]
    @inproceedings{BBB05,
    abstract = {In many real-world optimization problems, function values can only be estimated but not determined exactly. Falsely calibrated measurement instruments, inexact scales, scale reading errors, etc. are typical sources for measurement errors. If the function of interest is the output from stochastic simulations, then the measurements may be exact, but some of the model output variables are random variables. The term ``noise'' will be used in the remainder of this article to subsume these phenomena.
    This article discusses the performance of particle swarm optimization (PSO) algorithms on functions disturbed by Gaussian noise. It extends the analyses presented in [1] and [2] by also examining the influence of algorithm parameters, by considering a wider spectrum of noise levels, and analysing different types of noise (multiplicative and additive). Furthermore, we integrated a recently developed sequential sampling technique into the particle swarm optimization method. Similar techniques have been integrated into other metaheuristics [3, 4, 5], but their application to the PSO algorithm is new.},
    address = {Vienna, Austria},
    author = {Bartz-Beielstein, Thomas and Blum, Daniel and Branke, J{\"u}rgen},
    booktitle = {Proceedings 6th Metaheuristics International Conference (MIC2005)},
    date-added = {2015-11-29T01:38:43GMT},
    date-modified = {2017-03-07 23:12:31 +0000},
    editor = {Hartl, Richard and Doerner, Karl},
    keywords = {bartzPublic, free},
    pages = {89--94},
    rating = {0},
    title = {{Particle Swarm Optimization and Sequential Sampling in Noisy Environments}},
    year = {2005},
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  • [PDF] [DOI] Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss. Sequential Parameter Optimization. In B. McKay and others, editors, Proceedings 2005 Congress on Evolutionary Computation (CEC’05), Edinburgh, Scotland, page 773–780, Piscataway NJ, 2005. IEEE Press.
    [Bibtex]
    @inproceedings{BLP05,
    abstract = {Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. To demonstrate its flexibility, three scenarios are discussed: (1) no experience how to choose...},
    address = {Piscataway NJ},
    author = {Bartz-Beielstein, Thomas and Lasarczyk, Christian and Preuss, Mike},
    booktitle = {{Proceedings 2005 Congress on Evolutionary Computation (CEC'05), Edinburgh, Scotland}},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2021-07-22 12:12:43 +0200},
    doi = {10.1109/CEC.2005.1554761},
    editor = {McKay, B and others},
    isbn = {0-7803-9363-5},
    issn = {1089-778X},
    keywords = {bartzPublic, Bart16n, Bart16e, free, hein17a, frie17a, Bart19g},
    language = {English},
    pages = {773--780},
    publisher = {{IEEE Press}},
    rating = {0},
    read = {Yes},
    title = {{Sequential Parameter Optimization}},
    year = {2005},
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    bdsk-url-2 = {http://dx.doi.org/10.1109/CEC.2005.1554761}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Mike Preuss, and Sandor Markon. Validation and optimization of an elevator simulation model with modern search heuristics. In T. Ibaraki, K. Nonobe, and M. Yagiura, editors, Metaheuristics: progress as real problem solvers, page 109–128. Springer, Berlin, Heidelberg, New York, 2005.
    [Bibtex]
    @incollection{BPM05,
    abstract = {
    Abstract: Elevator supervisory group control (ESGC) is a complex combinatorial optimization task that can be solved by modern search heuristics. To reduce its complexity and to enable a theoretical analysis, a simplified ESGC model (S-ring) is proposed. The S-ring has many desirable properties: Fast evaluation, reproducibility, scalability, and extensibility. It can be described as a Markov decision process and thus be analyzed theoretically and numerically. Algorithm based validation (ABV), as a new methodology for the validation of simulation models, is introduced. Based on ABV, we show that the S-ring is a valid ESGC model. Finally, the extensibility of the S-ring model is demonstrated.},
    address = {Berlin, Heidelberg, New York},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Markon, Sandor},
    booktitle = {Metaheuristics: Progress as Real Problem Solvers},
    date-added = {2015-11-29T01:39:03GMT},
    date-modified = {2018-10-26 18:18:47 +0200},
    doi = {\url{10.1007/0-387-25383-1\_5}},
    editor = {Ibaraki, T and Nonobe, K and Yagiura, M},
    isbn = {0-387-25382-3},
    keywords = {bartzPublic, free},
    pages = {109--128},
    publisher = {Springer},
    rating = {0},
    title = {{Validation and optimization of an elevator simulation model with modern search heuristics}},
    year = {2005},
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    bdsk-url-2 = {http://dx.doi.org/10.1007/0-387-25383-1%5C_5%7D}}
  • Jörn Mehnen, Thomas Michelitsch, Christian W. G. Lasarczyk, and Thomas Bartz-Beielstein. Multiobjective Evolutionary Design of Mold Temperature Control using DACE for Parameter Optimization. In H. Pfützner and E. Leiss, editors, Proceedings twelfth international symposium interdisciplinary electromagnetics, mechanics, and biomedical problems (isem 2005), page 464–465, Vienna, Austria, 2005. Vienna magnetics group reports.
    [Bibtex]
    @inproceedings{Mehn05a,
    address = {Vienna, Austria},
    author = {Mehnen, J{\"o}rn and Michelitsch, Thomas and Lasarczyk, Christian W G and Bartz-Beielstein, Thomas},
    booktitle = {Proceedings Twelfth International Symposium Interdisciplinary Electromagnetics, Mechanics, and Biomedical Problems (ISEM 2005)},
    date-added = {2015-11-29T01:41:50GMT},
    date-modified = {2017-01-14 15:27:26 +0000},
    editor = {Pf{\"u}tzner, H and Leiss, E},
    keywords = {bartzPublic, nonfree},
    pages = {464--465},
    publisher = {Vienna Magnetics Group Reports},
    rating = {0},
    title = {{Multiobjective Evolutionary Design of Mold Temperature Control using DACE for Parameter Optimization}},
    year = {2005}}

2004

  • [PDF] Thomas Bartz-Beielstein, Konstantinos E. Parsopoulos, and Michael N. Vrahatis. Analysis of Particle Swarm Optimization Using Computational Statistics. In T. E. Simos and Ch Tsitouras, editors, Proceedings international conference numerical analysis and applied mathematics (icnaam), page 34–37, Weinheim, Germany, 2004. Wiley-vch.
    [Bibtex]
    @inproceedings{BPV04,
    abstract = {We propose a new methodology for the experimental analysis of evolutionary optimization algorithms. The proposed technique employs computational statistic methods to investigate the interactions among opti- mization problems, algorithms, and environments. The technique is applied for the parameterization of the Particle Swarm Optimization algorithm. An elevator supervisory group control system is introduced as a test case to provide intuition regarding the performance of the proposed approach in highly complex real--world problems.},
    address = {Weinheim, Germany},
    author = {Bartz-Beielstein, Thomas and Parsopoulos, Konstantinos E and Vrahatis, Michael N},
    booktitle = {Proceedings International Conference Numerical Analysis and Applied Mathematics (ICNAAM)},
    date-added = {2015-11-29T01:39:02GMT},
    date-modified = {2017-03-06 22:31:18 +0000},
    editor = {Simos, T E and Tsitouras, Ch},
    keywords = {bartzPublic},
    pages = {34--37},
    publisher = {Wiley-VCH},
    rating = {0},
    title = {{Analysis of Particle Swarm Optimization Using Computational Statistics}},
    year = {2004},
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  • [PDF] [DOI] Thomas Bartz-Beielstein, Konstantinos E. Parsopoulos, and Michael N. Vrahatis. Design and analysis of optimization algorithms using computational statistics. Applied numerical analysis and computational mathematics (anacm), 1(2):413–433, Dec 2004.
    [Bibtex]
    @article{BPV04b,
    abstract = {We propose a highly flexible sequential methodology for the experimental analysis of optimization algorithms. The proposed technique employs computational statistic methods to investigate the interactions among opti- mization problems, algorithms, and environments. The workings of the proposed technique are illustrated on the parameterization and comparison of both a population--based and a direct search algorithm, on a well-- known benchmark problem, as well as on a simplified model of a real--world problem. Experimental results are reported and conclusions are derived.},
    author = {Bartz-Beielstein, Thomas and Parsopoulos, Konstantinos E and Vrahatis, Michael N},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2021-07-24 10:06:01 +0200},
    doi = {10.1002/anac.200410007},
    groups = {bart16n},
    journal = {Applied Numerical Analysis and Computational Mathematics (ANACM)},
    keywords = {Bart16n, bartzPublic},
    month = {Dec},
    number = {2},
    pages = {413--433},
    rating = {0},
    title = {Design and Analysis of Optimization Algorithms Using Computational Statistics},
    volume = {1},
    year = {2004},
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    bdsk-url-1 = {https://doi.org/10.1002/anac.200410007}}
  • [PDF] [DOI] Thomas Bartz-Beielstein, Marcel de Vegt, Konstantinos E. Parsopoulos, and Michael N. Vrahatis. Designing Particle Swarm Optimization with Regression Trees. Technical Report, 05 2004.
    [Bibtex]
    @techreport{BVPV04,
    author = {Bartz-Beielstein, Thomas and de Vegt, Marcel and Parsopoulos, Konstantinos E and Vrahatis, Michael N},
    date-added = {2015-11-29T01:39:18GMT},
    date-modified = {2021-07-25 21:36:32 +0200},
    doi = {10.17877/DE290R-5432},
    keywords = {bartzPublic, free},
    month = 05,
    publisher = {Universit{\"a}t Dortmund, Germany},
    rating = {0},
    title = {{Designing Particle Swarm Optimization with Regression Trees}},
    url = {http://hdl.handle.net/2003/5469},
    year = {2004},
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    bdsk-url-1 = {http://hdl.handle.net/2003/5469},
    bdsk-url-2 = {http://dx.doi.org/10.17877/DE290R-5432}}
  • [PDF] Thomas Bartz-Beielstein, Karlheinz Schmitt, Jörn Mehnen, Boris Naujoks, and Dmytro Zibold. KEA–-A Software Package for Development, Analysis, and Application of Multiple Objective Evolutionary Algorithms. Technical Report CI-185/04, Universität Dortmund, Germany, nov 2004.
    [Bibtex]
    @techreport{BSMN04,
    abstract = {A software package for development, analysis and application of multi- objective evolutionary algorithms is described. The object-oriented design of this kit for evolutionary algorithms (KEA) offers a good suitable envi- ronment for various kinds of optimization tasks. It provides an interface to evaluate multi-objective fitness functions written in Java or C/C++ using a variety of multi-objective evolutionary algorithms (MOEA). In addition KEAcontains several state-of-the-art comparison methods for performance measure of algorithms. Furthermore KEAis able to display the progress of optimization in a dynamic display or just to display the results of optimiza- tion in a static visualization mode.
    This paper introduces the main concepts of the KEA-tool. Examples illus- trate how to work with it and how to extend its functionality.},
    author = {Bartz-Beielstein, Thomas and Schmitt, Karlheinz and Mehnen, J{\"o}rn and Naujoks, Boris and Zibold, Dmytro},
    date-added = {2015-11-29T01:39:18GMT},
    date-modified = {2017-03-07 21:59:10 +0000},
    institution = {Universit{\"a}t Dortmund, Germany},
    keywords = {bartzPublic, free},
    month = nov,
    number = {CI-185/04},
    rating = {0},
    title = {{KEA---A Software Package for Development, Analysis, and Application of Multiple Objective Evolutionary Algorithms}},
    year = {2004},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein and Boris Naujoks. Tuning Multicriteria Evolutionary Algorithms for Airfoil Design Optimization. Technical Report CI-159/04, Universität Dortmund, Germany, feb 2004.
    [Bibtex]
    @techreport{BaNa04,
    author = {Bartz-Beielstein, Thomas and Naujoks, Boris},
    date-added = {2015-11-29T01:38:58GMT},
    date-modified = {2017-01-14 12:49:20 +0000},
    institution = {Universit{\"a}t Dortmund, Germany},
    keywords = {bartzPublic, free},
    month = feb,
    number = {CI-159/04},
    rating = {0},
    title = {{Tuning Multicriteria Evolutionary Algorithms for Airfoil Design Optimization}},
    year = {2004},
    bdsk-file-1 = {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}}
  • [PDF] [DOI] Thomas Bartz-Beielstein and Sandor Markon. Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach. In G. W. Greenwood, editor, Proceedings 2004 congress on evolutionary computation (cec’04), portland or, page 1111–1118, Piscataway NJ, 2004. Ieee.
    [Bibtex]
    @inproceedings{BaBM04,
    address = {Piscataway NJ},
    author = {Bartz-Beielstein, Thomas and Markon, Sandor},
    booktitle = {Proceedings 2004 Congress on Evolutionary Computation (CEC'04), Portland OR},
    date-added = {2015-11-29T01:38:58GMT},
    date-modified = {2016-11-15 13:12:25 +0000},
    doi = {10.1109/CEC.2004.1330986},
    editor = {Greenwood, G W},
    isbn = {0-7803-8515-2},
    keywords = {bartzPublic},
    pages = {1111--1118},
    publisher = {IEEE},
    rating = {0},
    title = {{Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach}},
    url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1330986},
    year = {2004},
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    bdsk-url-1 = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1330986},
    bdsk-url-2 = {http://dx.doi.org/10.1109/CEC.2004.1330986}}
  • [DOI] Jörn Mehnen, Thomas Michelitsch, Thomas Bartz-Beielstein, and Karlheinz Schmitt. Evolutionary Optimization of Mould Temperature Control Strategies: Encoding and Solving the Multiobjective Problem with Standard Evolution Strategy and Kit for Evolutionary Algorithms. Journal of engineering manufacture (jem), 218(B6):657–665, 2004.
    [Bibtex]
    @article{MMBS04,
    author = {Mehnen, J{\"o}rn and Michelitsch, Thomas and Bartz-Beielstein, Thomas and Schmitt, Karlheinz},
    date-added = {2015-11-29T01:41:48GMT},
    date-modified = {2016-11-15 13:20:51 +0000},
    doi = {10.1243/0954405041167130},
    journal = {Journal of Engineering Manufacture (JEM)},
    keywords = {bartzPublic, nonfree},
    number = {B6},
    pages = {657--665},
    rating = {0},
    title = {{Evolutionary Optimization of Mould Temperature Control Strategies: Encoding and Solving the Multiobjective Problem with Standard Evolution Strategy and Kit for Evolutionary Algorithms}},
    url = {http://pib.sagepub.com/content/218/6/657.full.pdf},
    volume = {218},
    year = {2004},
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    bdsk-url-1 = {http://dx.doi.org/10.1243/0954405041167130},
    bdsk-url-2 = {http://pib.sagepub.com/content/218/6/657.full.pdf}}
  • [PDF] Jörn Mehnen, Thomas Michelitsch, Thomas Bartz-Beielstein, and Nadine Henkenjohann. Systematic analyses of multi-objective evolutionary algorithms applied to real-world problems using statistical design of experiments. In R. Teti, editor, Proceedings fourth international seminar intelligent computation in manufacturing engineering, page 171–178, Naples, Italy, 2004. Cirp icme’04.
    [Bibtex]
    @inproceedings{MMBH04,
    abstract = {Solving multi-objective optimization problems is a challenging task that demands efficient software tools and systematic analytical approaches. In this paper two evolutionary multi-objective optimization algorithms -- namely the evolution strategy (ES) and the NSGA II -- are applied to two complex real-world problems. The parameter settings of the evolutionary algorithms have been chosen and optimized according to statistical design plans. A new ranking method for measuring the quality of pareto-fronts is introduced. The layout of mold temperature control systems and the scheduling of elevators show typical complexity aspects that are necessary to illustrate a systematic approach of solving real-world multi-objective optimization problems.},
    address = {Naples, Italy},
    author = {Mehnen, J{\"o}rn and Michelitsch, Thomas and Bartz-Beielstein, Thomas and Henkenjohann, Nadine},
    booktitle = {Proceedings Fourth International Seminar Intelligent Computation in Manufacturing Engineering},
    date-added = {2015-11-29T01:41:44GMT},
    date-modified = {2017-03-08 22:57:46 +0000},
    editor = {Teti, R},
    keywords = {bartzPublic, free},
    pages = {171--178},
    publisher = {CIRP ICME'04},
    rating = {0},
    title = {{Systematic analyses of multi-objective evolutionary algorithms applied to real-world problems using statistical design of experiments}},
    year = {2004},
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  • Klaus Weinert, Jörn Mehnen, Thomas Michelitsch, Karlheinz Schmitt, and Thomas Bartz-Beielstein. A Multiobjective Approach to Optimize Temperature Control Systems of Molding Tools. Production engineering research and development, annals of the german academic society for production engineering, XI(1):77–80, 2004.
    [Bibtex]
    @article{WMMS04,
    author = {Weinert, Klaus and Mehnen, J{\"o}rn and Michelitsch, Thomas and Schmitt, Karlheinz and Bartz-Beielstein, Thomas},
    date-added = {2015-11-29T01:43:27GMT},
    date-modified = {2017-01-14 15:30:58 +0000},
    journal = {Production Engineering Research and Development, Annals of the German Academic Society for Production Engineering},
    keywords = {bartzPublic, nonfree},
    number = {1},
    pages = {77--80},
    rating = {0},
    title = {{A Multiobjective Approach to Optimize Temperature Control Systems of Molding Tools}},
    volume = {XI},
    year = {2004}}

2003

  • [PDF] Thomas Bartz-Beielstein, Sandor Markon, and Mike Preuss. Algorithm Based Validation of a Simplified Elevator Group Controller Model. In T. Ibaraki, editor, Proceedings 5th metaheuristics international conference (mic’03), pages 06/1–06/13 (CD–ROM), Kyoto, Japan, 2003.
    [Bibtex]
    @inproceedings{BMP03b,
    address = {Kyoto, Japan},
    author = {Bartz-Beielstein, Thomas and Markon, Sandor and Preuss, Mike},
    booktitle = {Proceedings 5th Metaheuristics International Conference (MIC'03)},
    date-added = {2015-11-29T01:38:58GMT},
    date-modified = {2016-11-15 13:31:11 +0000},
    editor = {Ibaraki, T},
    keywords = {bartzPublic, free},
    pages = {06/1--06/13 (CD--ROM)},
    rating = {0},
    title = {{Algorithm Based Validation of a Simplified Elevator Group Controller Model}},
    year = {2003},
    bdsk-file-1 = {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}}
  • [PDF] Thomas Bartz-Beielstein, Mike Preuss, and Andreas Reinholz. Evolutionary algorithms for optimization practitioners. Technical Report, Universität Dortmund, Germany, 7 2003.
    [Bibtex]
    @techreport{BPR03,
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Reinholz, Andreas},
    date-added = {2015-11-29T01:39:07GMT},
    date-modified = {2016-11-15 13:26:47 +0000},
    institution = {Universit{\"a}t Dortmund, Germany},
    keywords = {bartzPublic, free},
    month = 7,
    rating = {0},
    title = {{Evolutionary algorithms for optimization practitioners}},
    year = {2003},
    bdsk-file-1 = {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},
    bdsk-file-2 = {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}}
  • Thomas Bartz-Beielstein, Mike Preuss, and Andreas Reinholz. Evolutionary algorithms for optimization practitioners (Tutorial). In Proceedings 5th metaheuristics international conference (mic’03) kyoto, japan. Metaheuristics International Conference,, 8 2003.
    [Bibtex]
    @inproceedings{BPR03lecD,
    annote = {http://ls11-www.cs.uni-dortmund.de/people/tom. 3 September 2003},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Reinholz, Andreas},
    booktitle = {Proceedings 5th Metaheuristics International Conference (MIC'03) Kyoto, Japan},
    date-added = {2016-06-02T20:19:47GMT},
    date-modified = {2021-07-24 09:56:54 +0200},
    keywords = {bartzPublic, tutorial, BartzTutorial, free},
    month = 8,
    organization = {Metaheuristics International Conference},
    rating = {0},
    title = {{Evolutionary algorithms for optimization practitioners (Tutorial)}},
    year = {2003}}
  • [PDF] Thomas Bartz-Beielstein. Experimental Analysis of Evolution Strategies-Overview and Comprehensive Introduction. Reihe CI. SFB 531 157/03, University Dortmund, 11 2003.
    [Bibtex]
    @techreport{Bar03,
    abstract = {This article presents statistical techniques for the design and analysis of evolution strategies. These techniques can be applied to other search heuristics such as genetic algorithms, simulated annealing or par- ticle swarm optimizers. It provides guidelines for the comparison of differ- ent algorithms on artifical test functions and on real-world optimization problems. Statistical experimental design techniques to improve the in- tegrity and comparability of experiments are proposed. Interpreting the run of an optimization algorithm as an experiment, design of experi- ments (DOE), response surface methods (RSM), and tree-based regres- sion methods can be applied to analyze and to improve its performance. We recommmend to base the comparison of algorithms on ``tuned'' algo- rithms and not on their ``standard'' parameterizations.
    },
    author = {Bartz-Beielstein, Thomas},
    date-added = {2016-10-30 11:44:52 +0000},
    date-modified = {2021-07-24 09:59:39 +0200},
    institution = {University Dortmund},
    keywords = {bartzPublic, Bart16n, free},
    month = 11,
    number = {157/03},
    rating = {0},
    title = {{Experimental Analysis of Evolution Strategies-Overview and Comprehensive Introduction}},
    type = {Reihe CI. SFB 531},
    urn = {http://nbn-resolving.org/urn:nbn:de:101:1-201605122134},
    year = {2003},
    bdsk-file-1 = {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}}
  • [DOI] Thomas Bartz-Beielstein, Jörn Mehnen, Karlheinz Schmitt, Konstantinos E. Parsopoulos, and Michael N. Vrahatis. Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques. In R. Sarker and others, editors, Proceedings 2003 congress on evolutionary computation (cec’03), canberra, volume 3, page 1780–1787, Piscataway NJ, Dec 2003. Ieee.
    [Bibtex]
    @inproceedings{BLMS03b,
    abstract = {During the last decade, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article, a new particle swarm optimization technique, called DOPS, for multi-objective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research.},
    address = {Piscataway NJ},
    author = {Bartz-Beielstein, Thomas and Mehnen, J{\"o}rn and Schmitt, Karlheinz and Parsopoulos, Konstantinos E and Vrahatis, Michael N},
    booktitle = {Proceedings 2003 Congress on Evolutionary Computation (CEC'03), Canberra},
    date-added = {2015-11-29T01:38:58GMT},
    date-modified = {2019-08-18 21:04:46 +0200},
    doi = {10.1109/CEC.2003.1299888},
    editor = {Sarker, R and others},
    isbn = {0-7803-7804-0},
    keywords = {bartzPublic, free, Bart19g},
    month = {Dec},
    pages = {1780--1787},
    publisher = {IEEE},
    rating = {0},
    title = {{Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques}},
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  • [PDF] Thomas Bartz-Beielstein, Philip Limbourg, Jörn Mehnen, Karlheinz Schmitt, Kostas E. Parsopoulos, and Michael N. Vrahatis. Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques – Preprint. Technical Report, Universität Dortmund, Germany, 7 2003.
    [Bibtex]
    @techreport{BLMS03,
    author = {Bartz-Beielstein, Thomas and Limbourg, Philip and Mehnen, J{\"o}rn and Schmitt, Karlheinz and Parsopoulos, Kostas E and Vrahatis, Michael N},
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    institution = {Universit{\"a}t Dortmund, Germany},
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    title = {{Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques - Preprint}},
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  • [PDF] Thomas Bartz-Beielstein, Mike Preuss, and Sandor Markon. Validation and optimization of an elevator simulation model with modern search heuristics. Technical Report, Universität Dortmund, Germany, 12 2003.
    [Bibtex]
    @techreport{BPM03,
    abstract = {
    Abstract: Elevator supervisory group control (ESGC) is a complex combinatorial optimization task that can be solved by modern search heuristics. To reduce its complexity and to enable a theoretical analysis, a simplified ESGC model (S-ring) is proposed. The S-ring has many desirable properties: Fast evaluation, reproducibility, scalability, and extensibility. It can be described as a Markov decision process and thus be analyzed theoretically and numerically. Algorithm based validation (ABV), as a new methodology for the validation of simulation models, is introduced. Based on ABV, we show that the S-ring is a valid ESGC model. Finally, the extensibility of the S-ring model is demonstrated.},
    author = {Bartz-Beielstein, Thomas and Preuss, Mike and Markon, Sandor},
    date-added = {2015-11-29T01:39:07GMT},
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  • [PDF] Thomas Beielstein, Mike Preuss, and Sandor Markon. A Parallel Approach to Elevator Optimization. Technical Report, Technische Universität Dortmund, 2003.
    [Bibtex]
    @techreport{Beie03d,
    abstract = {Efficient elevator group control is a complex combinatorial optimization problem. Recent developments in this field include the use of reinforcement learning, fuzzy logic, neural networks and evolutionary algorithms [Mar95, CB98]. This paper summarizes the development of a parallel approach based on evolution strategies (ES) that is capable of optimizing the neuro- controller of an elevator group controller [SWW02]. It extends the architecture that was used for a simplified elevator group controller simulator [MAB+01, MN02, BEM03].
    Meta-heuristics might be useful as quick development techniques to create a new gener- ation of self-adaptive elevator group control systems that can handle high maximum traffic situations. Additionally, population based meta-heuristics such as evolution strategies can be easily parallelized. In the following we will consider a parallel elevator supervisory group con- trol (ESGC) system that is based on a set of neural network-driven controllers, one per elevator shaft. These may be situated in one or several different buildings as long as communication between controller instances is enabled.
    Since the ESGC problem is very costly in terms of computation time, a related dynamical system was introduced as simplified model: the sequential ring (S-ring) [MAB+01]. Using the S-ring also ensures that other researchers can compare their results with the ones presented here.
    },
    author = {Beielstein, Thomas and Preuss, Mike and Markon, Sandor},
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  • [PDF] Thomas Beielstein, Sandor Markon, and Mike Preuss. A Parallel Approach to Elevator Optimization Based on Soft Computing. In T. Ibaraki, editor, Proceedings 5th metaheuristics international conference (mic’03), pages 07/1–07/11 (CD–ROM), Kyoto, Japan, 2003.
    [Bibtex]
    @inproceedings{BMP03,
    abstract = {Efficient elevator group control is a complex combinatorial optimization problem. Recent developments in this field include the use of reinforcement learning, fuzzy logic, neural networks and evolutionary algorithms [Mar95, CB98]. This paper summarizes the development of a parallel approach based on evolution strategies (ES) that is capable of optimizing the neuro- controller of an elevator group controller [SWW02]. It extends the architecture that was used for a simplified elevator group controller simulator [MAB+01, MN02, BEM03].
    Meta-heuristics might be useful as quick development techniques to create a new gener- ation of self-adaptive elevator group control systems that can handle high maximum traffic situations. Additionally, population based meta-heuristics such as evolution strategies can be easily parallelized. In the following we will consider a parallel elevator supervisory group con- trol (ESGC) system that is based on a set of neural network-driven controllers, one per elevator shaft. These may be situated in one or several different buildings as long as communication between controller instances is enabled.
    Since the ESGC problem is very costly in terms of computation time, a related dynamical system was introduced as simplified model: the sequential ring (S-ring) [MAB+01]. Using the S-ring also ensures that other researchers can compare their results with the ones presented here.
    },
    address = {Kyoto, Japan},
    author = {Beielstein, Thomas and Markon, Sandor and Preuss, Mike},
    booktitle = {Proceedings 5th Metaheuristics International Conference (MIC'03)},
    date-added = {2015-11-29T01:39:36GMT},
    date-modified = {2017-03-06 22:22:00 +0000},
    editor = {Ibaraki, T},
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  • [PDF] Thomas Beielstein, Mike Preuss, and Sandor Markon. Algorithm based validation of a simplified elevator group controller model. Sonderforschungsbereich (SFB) 531, Technische Universität Dortmund, 2003.
    [Bibtex]
    @techreport{Beie03g,
    abstract = {Today's urban life cannot be imagined without elevators. The central part of an elevator system, the elevator group controller, assigns elevator cars to service calls in real-time while optimizing the overall service quality, the tra c throughput, and/or the energy consumption. The elevator supervisory group control (ESGC) problem can be classified as a combinatorial optimization problem. It reveals the same complex behavior as many other stochastic traffic control problems, i.e. materials handling systems (MHS) with automated guided vehicles (AGVs).
    Due to many di culties in analysis, design, simulation, and control, the ESGC problem has been studied for a long time. First approaches were mainly based on analytical approaches derived from queuing theory, whereas currently computational intelligence (CI) methods and other heuristics are accepted as state of the art.
    In this article we will propose a validation methodology for a simplified ESGC system, the sequential ring (S-ring). The S-ring is constructed as a simplified model of an ESGC system using a neural network (NN) to control the elevators. Some of the NN connection weights can be modified, so that different weight settings and their influence on the ESGC performance
    can be tested. The performance of one specific weight setting is based on simulations of specific tra c situations, which automatically lead to stochastically disturbed (noisy) fitness function values. The determination of an optimal weight setting is not trivial, since it is difficult to find an efficient strategy that modifies the weights without generating too many infeasible solutions, and to judge the performance or fitness of one ESGC configuration.},
    author = {Beielstein, Thomas and Preuss, Mike and Markon, Sandor},
    date-added = {2015-11-29T01:39:35GMT},
    date-modified = {2021-07-24 09:52:35 +0200},
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    title = {{Algorithm based validation of a simplified elevator group controller model}},
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  • [DOI] Thomas Beielstein, Jörn Mehnen, Lutz Schönemann, Hans–Paul Schwefel, Tobias Surmann, Klaus Weinert, and Dirk Wiesmann. Design of evolutionary algorithms and applications in surface reconstruction. In H. P. Schwefel, I. Wegener, and K. Weinert, editors, Advances in computational intelligence–-theory and practice, page 145–193. Springer, Berlin, Heidelberg, New York, 2003.
    [Bibtex]
    @incollection{BMSS03,
    address = {Berlin, Heidelberg, New York},
    author = {Beielstein, Thomas and Mehnen, J{\"o}rn and Sch{\"o}nemann, Lutz and Schwefel, Hans--Paul and Surmann, Tobias and Weinert, Klaus and Wiesmann, Dirk},
    booktitle = {Advances in Computational Intelligence---Theory and Practice},
    date-added = {2015-11-29T01:39:35GMT},
    date-modified = {2021-07-24 09:50:06 +0200},
    doi = {10.1007/978-3-662-05609-7_6},
    editor = {Schwefel, H P and Wegener, I and Weinert, K},
    isbn = {978-3-642-07758-6},
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    pages = {145--193},
    publisher = {Springer},
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    title = {Design of evolutionary algorithms and applications in surface reconstruction},
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    year = {2003},
    bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-662-05609-7_6}}
  • [PDF] [DOI] Thomas Beielstein, Claus–Peter Ewald, and Sandor Markon. Optimal Elevator Group Control by Evolution Strategies. In Cant E. u-Paz and others, editors, Proceedings genetic and evolutionary computation conf. (gecco2003), chicago il, part ii, page 1963–1974, Berlin, Heidelberg, New York, 2003. Springer.
    [Bibtex]
    @inproceedings{BEM03,
    abstract = {Efficient elevator group control is important for the oper- ation of large buildings. Recent developments in this field include the use of fuzzy logic and neural networks. This paper summarizes the de- velopment of an evolution strategy (ES) that is capable of optimizing the neuro-controller of an elevator group controller. It extends the re- sults that were based on a simplified elevator group controller simulator. A threshold selection technique is presented as a method to cope with noisy fitness function values during the optimization run. Experimental design techniques are used to analyze first experimental results.
    },
    address = {Berlin, Heidelberg, New York},
    author = {Beielstein, Thomas and Ewald, Claus--Peter and Markon, Sandor},
    booktitle = {Proceedings Genetic and Evolutionary Computation Conf. (GECCO2003), Chicago IL, Part II},
    date-added = {2015-11-29T01:39:36GMT},
    date-modified = {2021-07-24 09:55:34 +0200},
    doi = {10.5555/1756582.1756684},
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    keywords = {bartzPublic, nonfree},
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  • [PDF] [DOI] Thomas Beielstein. Tuning Evolutionary Algorithms–-Overview and Comprehensive Introduction. Technical Report, Technische Universität Dortmund, apr 2003.
    [Bibtex]
    @techreport{Bei03,
    abstract = {At present, it is intensely discussed, which type of experimental research method-ologies should be used to improve the acceptance and quality of evolutionary algorithms (EAs). A broad spectrum of presentation techniques makes new results in evolutionary computation (EC) almost incomparable . Discussions and sessions related to this subject took part during the congress on evolutionary computation (CEC) and on the genetic and evolutionary computation confer-ence (GECCO) . In [1], Eiben and Jelasity list explicitely four problems, that result from this situation:
    the lack of a standardized test-functions, or benchmark problems, the usage of different performance measures,
    the impreciseness of results, and therefore no clearly specified conclusions, and
    the lack of reproducibility of experiments.
    EC shares these problems with other scientific disciplines. Solutions from these other disciplines, that have been successfully applied for many years, might be transferable to EC. Here we can mention: statistical design of experiments [2]. design of computational experiments to test heuristics [3, 4]. experimental de- signs for simulation [5], or deterministic computer experiments [6].
    We suggest to use techniques that are well known in statistics under the name design of experiments (DOE) for many decades. In our approach, an experiment consists of a problem and its related fitness function, an algorithm , and a quality criterion: we will use design of experiments, regression analysis, and generalized linear models , to improve algorithm performance. The main focus in this paper lies on natural problem classes: its elements are problems that arc based on real-world optimization problems in contrast to artificial problem classes [1].},
    author = {Beielstein, Thomas},
    date-added = {2015-11-29T01:39:31GMT},
    date-modified = {2017-03-25 22:49:49 +0000},
    doi = {http://dx.doi.org/10.17877/DE290R-15364},
    institution = {Technische Universit{\"a}t Dortmund},
    keywords = {bartzPublic, free, hein17a},
    month = apr,
    publisher = {Universit{\"a}t Dortmund, Germany},
    rating = {0},
    title = {{Tuning Evolutionary Algorithms---Overview and Comprehensive Introduction}},
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2002

  • [DOI] Thomas Beielstein, Jan Dienstuhl, Christian Feist, and Marc Pompl. Circuit Design Using Evolutionary Algorithms. In D. B. Fogel and others, editors, Proceedings 2002 congress on evolutionary computation (cec’02) within third ieee world congress on computational intelligence (wcci’02), honolulu hi, page 1904–1909, Piscataway NJ, 2002. Ieee.
    [Bibtex]
    @inproceedings{BDFP02,
    abstract = {We demonstrate the applicability of evolutionary algorithms (EAs) to the optimization of circuit designs. We examine the design of a full-adder cell, and show the capability of design of experiments (DOE) methods to improve the parameter-settings of EAs.},
    address = {Piscataway NJ},
    author = {Beielstein, Thomas and Dienstuhl, Jan and Feist, Christian and Pompl, Marc},
    booktitle = {Proceedings 2002 Congress on Evolutionary Computation (CEC'02) Within Third IEEE World Congress on Computational Intelligence (WCCI'02), Honolulu HI},
    date-added = {2015-11-29T01:39:37GMT},
    date-modified = {2017-03-06 22:48:47 +0000},
    doi = {10.1109/CEC.2002.1004534},
    editor = {Fogel, D B and others},
    isbn = {0-7803-7282-4},
    keywords = {bartzPublic, nonfree},
    pages = {1904--1909},
    publisher = {IEEE},
    rating = {0},
    title = {{Circuit Design Using Evolutionary Algorithms}},
    uri = {\url{papers3://publication/doi/10.1109/CEC.2002.1004534}},
    url = {http://dx.doi.org/10.1109/CEC.2002.1004534},
    year = {2002},
    bdsk-url-1 = {http://dx.doi.org/10.1109/CEC.2002.1004534}}
  • [PDF] [DOI] Thomas Beielstein and Sandor Markon. Threshold Selection, Hypothesis Tests, and DOE Methods. Technical Report, Technische Universität Dortmund, 12 2002.
    [Bibtex]
    @techreport{BeMa02,
    abstract = {Threshold selection - a selection mechanism for noisy evolutionary algorithms - is put into the broader context of hypothesis testing. Theoretical results are presented and applied to a simple model of stochastic search and to a simplified elevator simulator. Design of experiments methods are used to validate the significance of the results.},
    author = {Beielstein, Thomas and Markon, Sandor},
    date-added = {2015-11-29T01:39:36GMT},
    date-modified = {2021-07-25 21:39:48 +0200},
    doi = {http://dx.doi.org/10.17877/DE290R-15303},
    institution = {Technische Universit{\"a}t Dortmund},
    keywords = {bartzPublic, free},
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    publisher = {Universit{\"a}t Dortmund, Germany},
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    title = {{Threshold Selection, Hypothesis Tests, and DOE Methods}},
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    year = {2002},
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    bdsk-url-1 = {http://hdl.handle.net/2003/5417%20http://hdl.handle.net/2003/5417%20http://hdl.handle.net/2003/5417%20http://hdl.handle.net/2003/5417},
    bdsk-url-2 = {http://hdl.handle.net/2003/5417},
    bdsk-url-3 = {http://dx.doi.org/10.17877/DE290R-15303}}
  • [DOI] Thomas Beielstein and Sandor Markon. Threshold Selection, Hypothesis Tests, and DOE Methods. In D. B. Fogel and others, editors, Proceedings 2002 congress on evolutionary computation (cec’02) within third ieee world congress on computational intelligence (wcci’02), honolulu hi, page 777–782, Piscataway NJ, 2002. Ieee.
    [Bibtex]
    @inproceedings{BeMa02b,
    address = {Piscataway NJ},
    author = {Beielstein, Thomas and Markon, Sandor},
    booktitle = {Proceedings 2002 Congress on Evolutionary Computation (CEC'02) Within Third IEEE World Congress on Computational Intelligence (WCCI'02), Honolulu HI},
    date-added = {2015-11-29T01:39:37GMT},
    date-modified = {2017-03-08 23:12:28 +0000},
    doi = {10.1109/CEC.2002.1007024},
    editor = {Fogel, D B and others},
    isbn = {0-7803-7282-4},
    keywords = {bartzPublic, nonfree},
    pages = {777--782},
    publisher = {IEEE},
    rating = {0},
    title = {{Threshold Selection, Hypothesis Tests, and DOE Methods}},
    uri = {\url{papers3://publication/doi/10.1109/CEC.2002.1007024}},
    url = {http://dx.doi.org/10.1109/CEC.2002.1007024},
    year = {2002},
    bdsk-url-1 = {http://dx.doi.org/10.1109/CEC.2002.1007024}}
  • [PDF] [DOI] Thomas Beielstein, Konstantinos E. Parsopoulos, and Michael N. Vrahatis. Tuning PSO parameters through sensitivity analysis. Technical Report, Universität Dortmund, Sonderforschungsbereich (SFB) 531, 01 2002.
    [Bibtex]
    @techreport{BPV02,
    abstract = {In this paper, a first analysis of the Particle Swarm Optimization (PSO) method's parameters, using Design of Experiments (DOE) techniques, is performed, and important settings as well as interactions among the parameters, are investigated (screening).},
    address = {Sonderforschungsbereich (SFB) 531},
    author = {Beielstein, Thomas and Parsopoulos, Konstantinos E and Vrahatis, Michael N},
    date-added = {2015-11-29T01:39:36GMT},
    date-modified = {2021-07-24 09:49:04 +0200},
    doi = {10.17877/DE290R-15305},
    institution = {Universit{\"a}t Dortmund},
    keywords = {bartzPublic, free},
    month = 01,
    publisher = {Universit{\"a}t Dortmund, Germany},
    rating = {0},
    title = {{Tuning PSO parameters through sensitivity analysis}},
    url = {http://hdl.handle.net/2003/5420},
    year = {2002},
    bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAeLi4vc2NpZWJvL1dlYnN0b3JlLmQvQlBWMDIucGRmTxEBRAAAAAABRAACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAAAAAAAEJEAAH/////CUJQVjAyLnBkZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAAAAAAAAAAAAAAQADAAAKIGN1AAAAAAAAAAAAAAAAAApXZWJzdG9yZS5kAAIAKS86VXNlcnM6YmFydHo6c2NpZWJvOldlYnN0b3JlLmQ6QlBWMDIucGRmAAAOABQACQBCAFAAVgAwADIALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASACdVc2Vycy9iYXJ0ei9zY2llYm8vV2Vic3RvcmUuZC9CUFYwMi5wZGYAABMAAS8AABUAAgAM//8AAAAIAA0AGgAkAEUAAAAAAAACAQAAAAAAAAAFAAAAAAAAAAAAAAAAAAABjQ==},
    bdsk-url-1 = {http://hdl.handle.net/2003/5420},
    bdsk-url-2 = {https://doi.org/10.17877/DE290R-15305}}

2001

  • [PDF] Thomas Bartz-Beielstein and others. Collaborative Research Center 531. Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods (Poster). 09 2001.
    [Bibtex]
    @misc{Beie01a,
    author = {Bartz-Beielstein, Thomas and {others}},
    date-added = {2015-11-29T01:39:03GMT},
    date-modified = {2021-07-25 21:50:57 +0200},
    keywords = {bartzPublic, free},
    month = 09,
    rating = {0},
    title = {{Collaborative Research Center 531. Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods (Poster)}},
    url = {http://www.cec.uchile.cl/~wsc6/},
    year = {2001},
    bdsk-file-1 = {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},
    bdsk-url-1 = {http://www.cec.uchile.cl/~wsc6/}}
  • [PDF] Thomas Beielstein, Jan Dienstuhl, Christian Feist, and Marc Pompl. Circuit Design Using Evolutionary Algorithms. Technical Report, Technische Universität Dortmund, 12 2001.
    [Bibtex]
    @techreport{BDFP01,
    author = {Beielstein, Thomas and Dienstuhl, Jan and Feist, Christian and Pompl, Marc},
    date-added = {2015-11-29T01:39:35GMT},
    date-modified = {2021-07-25 21:34:20 +0200},
    institution = {Technische Universit{\"a}t Dortmund},
    keywords = {bartzPublic, free},
    month = 12,
    publisher = {Universit{\"a}t Dortmund, Germany},
    rating = {0},
    title = {{Circuit Design Using Evolutionary Algorithms}},
    year = {2001},
    bdsk-file-1 = {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}}
  • [DOI] Sandor Markon, Dirk V. Arnold, Thomas Bäck, Thomas Beielstein, and Hans-Georg Beyer. Thresholding–-A selection operator for noisy ES. In J. H. Kim, B. T. Zhang, G. Fogel, and I. Kuscu, editors, Proceedings 2001 congress on evolutionary computation (cec’01), seoul, page 465–472, Piscataway NJ, 2001. Ieee.
    [Bibtex]
    @inproceedings{MABB01,
    abstract = {The starting point for the analysis and experiments presented in this paper is a simplified elevator control problem, called `S-ring'. As in many other real-world optimization problems, the exact fitness function evaluation is disturbed by noise. Evolution strategies (ES) can generally cope with noisy fitness function values. It has been proposed that the `plus'-strategy can find better solutions by keeping over-valued function values, thus preventing inferior offspring with fitness inflated by noise from being accepted. The `plus'-strategy builds an implicit barrier around the current best population. We propose to make this barrier building process explicit and to employ a threshold value $\tau$ to be used in a selection operator for noisy fitness functions. `Thresholding' accepts a new individual if its apparent fitness is better than that of the parent by at least the margin $\tau$. First analytical investigations and empirical results from tests on the sphere-model and `S-ring' are presented},
    address = {Piscataway NJ},
    author = {Markon, Sandor and Arnold, Dirk V and B{\"a}ck, Thomas and Beielstein, Thomas and Beyer, Hans-Georg},
    booktitle = {Proceedings 2001 Congress on Evolutionary Computation (CEC'01), Seoul},
    date-added = {2015-11-29T01:41:49GMT},
    date-modified = {2017-01-14 16:11:45 +0000},
    doi = {10.1109/CEC.2001.934428},
    editor = {Kim, J H and Zhang, B T and Fogel, G and Kuscu, I},
    isbn = {0-7803-6657-3},
    keywords = {bartzPublic, nonfree},
    pages = {465--472},
    publisher = {IEEE},
    rating = {0},
    title = {{Thresholding---A selection operator for noisy ES}},
    url = {http://dx.doi.org/10.1109/CEC.2001.934428},
    year = {2001},
    bdsk-url-1 = {http://dx.doi.org/10.1109/CEC.2001.934428}}

2000

  • [PDF] Horst F. Wedde and Thomas Beielstein. Informatik an einer Waldorfschule: Ziele, Erfahrungen, Perspektiven. Erziehungskunst, 6:678–687, 2000.
    [Bibtex]
    @article{Wedd00a,
    author = {Wedde, Horst F and Beielstein, Thomas},
    date-added = {2015-11-29T01:43:28GMT},
    date-modified = {2017-01-14 16:12:58 +0000},
    journal = {Erziehungskunst},
    keywords = {bartzPublic, free},
    month = jun,
    pages = {678--687},
    rating = {0},
    title = {{Informatik an einer Waldorfschule: Ziele, Erfahrungen, Perspektiven}},
    volume = {6},
    year = {2000},
    bdsk-file-1 = {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}}

1995

  • [PDF] Thomas Bäck, Thomas Beielstein, Boris Naujoks, and Jochen Heistermann. Evolutionary Algorithms for the Optimization of Simulation Models using PVM. In J. Dongarra, M. Gengler, B. Tourancheau, and X. Vigouroux, editors, Second european pvm users’ group meeting (europvm’95), page 277–282, Paris, France, 1995. Hermès.
    [Bibtex]
    @inproceedings{BBNH95,
    abstract = {Simulation models usually share some specific characteristics that make the automatic optimization of their input parameters an extremely difficult task. Evolutionary algorithms--- search and optimization methods gleaned from the model of organic evolution--- are applicable to this problem and known to be able to yield good solutions for many difficult practical optimization problems. The paper presents a parallel, steady-state evolutionary algorithm which exploits the available parallel machine configuration in an optimal manner. The algorithm is implemented under PVM and runs in a LAN of SUN SPARC workstations. The basic algorithm is applicable to arbitrary simulation models, and only the individual structure and the genetic operators must be specified for a particular application. As an application example, the problem of optimizing pressurized water reactor core reload designs is briefly discussed and first experimental results are presented.},
    address = {Paris, France},
    author = {B{\"a}ck, Thomas and Beielstein, Thomas and Naujoks, Boris and Heistermann, Jochen},
    booktitle = {Second European PVM Users' Group Meeting (EuroPVM'95)},
    date-added = {2015-11-29T01:38:05GMT},
    date-modified = {2021-07-24 09:43:42 +0200},
    editor = {Dongarra, J and Gengler, M and Tourancheau, B and Vigouroux, X},
    keywords = {bartzPublic, free},
    pages = {277--282},
    publisher = {Herm{\`e}s},
    rating = {0},
    title = {{Evolutionary Algorithms for the Optimization of Simulation Models using PVM}},
    year = {1995},
    bdsk-file-1 = {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}}
  • Markus Höhfeld, Jochen Heistermann, Cornelia Kappler, Helge Rosé, Thomas Bäck, Thomas Beielstein, and Boris Naujoks. Anwendungen evolutionärer Algorithmen. In G. Wolf, R. Schmidt, and M. van der Meer, editors, Statusseminar des bmbf: bioinformatik, page 281–292, Berlin, Germany, 1995. Projektträger informationstechnik des bmbf bei der deutschen forschungsanstalt für luft- und raumfahrt e.v..
    [Bibtex]
    @inproceedings{HHKR95,
    address = {Berlin, Germany},
    author = {H{\"o}hfeld, Markus and Heistermann, Jochen and Kappler, Cornelia and Ros{\'e}, Helge and B{\"a}ck, Thomas and Beielstein, Thomas and Naujoks, Boris},
    booktitle = {Statusseminar des BMBF: Bioinformatik},
    date-added = {2015-11-29T01:40:33GMT},
    date-modified = {2017-01-14 16:09:24 +0000},
    editor = {Wolf, G and Schmidt, R and van der Meer, M},
    keywords = {bartzPublic, free},
    pages = {281--292},
    publisher = {Projekttr{\"a}ger Informationstechnik des BMBF bei der Deutschen Forschungsanstalt f{\"u}r Luft- und Raumfahrt e.V.},
    rating = {0},
    title = {{Anwendungen evolution{\"a}rer Algorithmen}},
    year = {1995}}