Publications

Publications SPOTSeven

2017

  • [PDF] T. Bartz-Beielstein, L. Gentile, and M. Zaefferer, “In a nutshell: sequential parameter optimization,” {TH} Köln, 7/2017, 2017.
    [Bibtex]
    @techreport{Bart17pcos,
    Author = {Bartz-Beielstein, Thomas and Gentile, Lorenzo and Zaefferer, Martin},
    Date-Added = {2017-11-02 18:28:59 +0000},
    Date-Modified = {2017-12-12 13:40:16 +0000},
    Institution = {{TH} K{\"o}ln},
    Keywords = {bartzPublic},
    Month = {Dec},
    Note = {Cologne Open Science},
    Number = {7/2017},
    Title = {In a Nutshell: Sequential Parameter Optimization},
    Year = {2017},
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  • [DOI] M. Zaefferer, A. Fischbach, B. Naujoks, and T. Bartz-Beielstein, “Simulation-based test functions for optimization algorithms,” in Proceedings of the genetic and evolutionary computation conference, New York, NY, USA, 2017, pp. 905-912.
    [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},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/3071178.3071190},
    Bdsk-Url-2 = {http://dx.doi.org/10.1145/3071178.3071190}}
  • S. Moritz, T. Bartz-Beielstein, J. Strohschein, R. Seger, and D. Gross, “Trinkwasser-sicherheit mit predictive analytics und oracle,” {TH} Köln, {CIOP} Technical Report 4/2017, 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 = {2017-08-17 10:50:15 +0000},
    Institution = {{TH} K{\"o}ln},
    Keywords = {bartzPublic},
    Number = {4/2017},
    Title = {Trinkwasser-Sicherheit mit Predictive Analytics und Oracle},
    Type = {{CIOP} Technical Report},
    Url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4869},
    Year = {2017},
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    Bdsk-Url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4869}}
  • [PDF] M. Zaefferer, A. Fischbach, B. Naujoks, and T. Bartz-Beielstein, “Simulation-based test functions for optimization algorithms,” Fakultät für Informatik und Ingenieurwissenschaften (F10), 3/2017, 2017.
    [Bibtex]
    @techreport{zaef17acos,
    Author = {Martin Zaefferer and Andreas Fischbach and Boris Naujoks and Thomas Bartz-Beielstein},
    Date-Added = {2017-07-07 12:48:25 +0000},
    Date-Modified = {2017-09-14 20:22:52 +0000},
    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},
    Url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4777},
    Year = {2017},
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  • J. Heinerman, J. Stork, M. A. R. Coy, J. Hubert, A. E. Eiben, T. Bartz-Beielstein, and E. Haasdijk, “Can social learning increase learning speed, performance or both?,” in Ecal ’17: proceedings of the 2017 conference, 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 = {ECAL '17: Proceedings of the 2017 Conference},
    Date-Added = {2017-06-06 18:35:44 +0000},
    Date-Modified = {2017-06-06 18:37:37 +0000},
    Keywords = {bartzPublic, nonfree},
    Title = {Can Social Learning Increase Learning Speed, Performance or Both?},
    Year = {2017}}
  • [PDF] [DOI] T. Bartz-Beielstein, J. Blaurock, S. Krey, Y. Fu, N. Kallenbach, and M. Möller, “Structural Health Monitoring von Faserverbundstrukturen mittels Piezosensoren – Untersuchungen zum experimentellen Design,” Fakultät für Informatik und Ingenieurwissenschaften (F10), 2/2017, 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 = {2017-06-04 11:51:58 +0000},
    Doi = {10.13140/RG.2.2.27207.09126},
    Institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
    Keywords = {bartzPublic, free},
    Number = {2/2017},
    Pages = {22},
    Series = {CIplus},
    Title = {{Structural Health Monitoring von Faserverbundstrukturen mittels Piezosensoren - Untersuchungen zum experimentellen Design}},
    Url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4727},
    Year = {2017},
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  • S. Moritz and T. Bartz-Beielstein, “imputeTS: Time Series Missing Value Imputation in R,” The R Journal, 2017.
    [Bibtex]
    @article{Mori17a,
    Author = {Steffen Moritz and Thomas Bartz-Beielstein},
    Date-Added = {2017-04-10 21:03:47 +0000},
    Date-Modified = {2017-04-10 21:05:12 +0000},
    Journal = {{The R Journal}},
    Keywords = {bartzPublic, free},
    Title = {{imputeTS: Time Series Missing Value Imputation in R}},
    Url = {https://journal.r-project.org/archive/2017/RJ-2017-009/index.html},
    Year = {2017},
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    Bdsk-Url-1 = {https://journal.r-project.org/archive/2017/RJ-2017-009/index.html}}
  • [DOI] A. Sardá-Espinosa, S. Subbiah, and T. Bartz-Beielstein, “Conditional inference trees for knowledge extraction from motor health condition data,” Engineering applications of artificial intelligence, vol. 62, pp. 26-37, 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] T. Bartz-Beielstein, M. Zaefferer, J. Stork, and S. Krey, “The revised sequential parameter optimization toolbox,” in The R User Conference, useR! 2017, 2017, p. 151.
    [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. http://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},
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  • J. Heinerman, J. Stork, M. A. R. Coy, J. Hubert, T. Bartz-Beielstein, A. E. Eiben, and E. Haasdijk, “Is social learning more than parameter tuning?,” in Gecco ’17: proceedings of the 2017 annual conference on genetic and evolutionary computation, 2017.
    [Bibtex]
    @inproceedings{Hein17a,
    Author = {Jacqueline Heinerman and J{\"o}rg Stork and Margarita Alejandra Rebolledo Coy and Julien Hubert and Thomas Bartz-Beielstein and A.E. Eiben and Evert Haasdijk},
    Booktitle = {GECCO '17: Proceedings of the 2017 Annual Conference on Genetic and Evolutionary Computation},
    Date-Added = {2017-03-24 17:11:06 +0000},
    Date-Modified = {2017-04-21 10:55:14 +0000},
    Keywords = {bartzPublic, nonfree},
    Title = {Is Social Learning More Than Parameter Tuning?},
    Year = {2017}}
  • [DOI] T. Bartz-Beielstein and M. Zaefferer, “Model-based methods for continuous and discrete global optimization,” Applied soft computing, vol. 55, pp. 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] T. Bartz-Beielstein, S. Moritz, J. Strohschein, T. Winterberg, D. Gross, and R. Seger, “Trinkwassersicherheit mit Predictive Analytics und Oracle,” DOAG news, iss. 1, pp. 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}}

2016

  • [PDF] [DOI] M. Zaefferer and T. Bartz-Beielstein, “Efficient global optimization with indefinite kernels,” in Parallel problem solving from nature — ppsn xiv: 14th international conference, edinburgh, uk, september 17-21, 2016, proceedings, J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, and B. Paechter, Eds., Cham: Springer International Publishing, 2016, pp. 69-79.
    [Bibtex]
    @inbook{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 = {2017-03-07 09:27:47 +0000},
    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}}
  • [DOI] C. Doerr, N. Bredeche, E. Alba, T. Bartz-Beielstein, D. Brockhoff, B. Doerr, G. Eiben, M. G. Epitropakis, C. M. Fonseca, A. Guerreiro, E. Haasdijk, J. Heinerman, J. Hubert, P. K. Lehre, L. Malagò, J. J. Merelo, J. Miller, B. Naujoks, P. Oliveto, S. Picek, N. Pillay, M. Preuss, P. Ryser-Welch, G. Squillero, J. Stork, D. Sudholt, A. Tonda, D. Whitley, and M. Zaefferer, “Tutorials at ppsn 2016,” in Parallel problem solving from nature — ppsn xiv: 14th international conference, edinburgh, uk, september 17-21, 2016, proceedings, J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, and B. Paechter, Eds., Springer International Publishing, 2016, pp. 1012-1022.
    [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 = {2017-02-14 11:28:14 +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}}
  • [PDF] A. Fischbach, M. Zaefferer, J. Stork, M. Friese, and T. Bartz-Beielstein, “From real world data to test functions,” in Proceedings. 26. workshop computational intelligence, Dortmund, 2016, pp. 159-177.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein, H. Stenzel, M. Zaefferer, B. Breiderhoff, Q. C. Pham, D. Gusew, A. Mengi, B. Kabacali, J. Tünte, L. Büscher, S. Wüstlich, and T. Friesen, Optimization of the cyclone separator geometry via multimodel simulation, 2016.
    [Bibtex]
    @misc{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 = {2017-06-04 11:57:24 +0000},
    Institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
    Keywords = {bartzPublic},
    Number = {9/2016},
    Pages = {28},
    Series = {CIplus},
    Title = {Optimization of the Cyclone Separator Geometry via Multimodel Simulation},
    Url = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4380},
    Year = {2016},
    Bdsk-File-1 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YV5iYXJ0MTZlY29zLnBkZtIXCxgZV05TLmRhdGFPEQFWAAAAAAFWAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8OYmFydDE2ZWNvcy5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAIAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgAuLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpiYXJ0MTZlY29zLnBkZgAOAB4ADgBiAGEAcgB0ADEANgBlAGMAbwBzAC4AcABkAGYADwAaAAwATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgAsVXNlcnMvYmFydHovc2NpZWJvL1dlYnN0b3JlLmQvYmFydDE2ZWNvcy5wZGYAEwABLwAAFQACAAz//wAAgAbSGxwdHlokY2xhc3NuYW1lWCRjbGFzc2VzXU5TTXV0YWJsZURhdGGjHR8gVk5TRGF0YVhOU09iamVjdNIbHCIjXE5TRGljdGlvbmFyeaIiIF8QD05TS2V5ZWRBcmNoaXZlctEmJ1Ryb290gAEACAARABoAIwAtADIANwBAAEYATQBVAGAAZwBqAGwAbgBxAHMAdQB3AIQAjgCdAKIAqgIEAgYCCwIWAh8CLQIxAjgCQQJGAlMCVgJoAmsCcAAAAAAAAAIBAAAAAAAAACgAAAAAAAAAAAAAAAAAAAJy},
    Bdsk-Url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4380}}
  • [PDF] T. Bartz-Beielstein, “EASD-experimental algorithmics for streaming data,” TH Köln, 2/2016, 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 = {2017-03-07 09:23:12 +0000},
    Institution = {TH K{\"o}ln},
    Keywords = {bartzPublic, free},
    Number = {2/2016},
    Title = {{EASD}-Experimental Algorithmics for Streaming Data},
    Year = {2016},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein and M. Zaefferer, “Model-based methods for continuous and discrete global optimization,” Fakultät für Informatik und Ingenieurwissenschaften (F10), TH Köln, Schriftenreihe CIplus 8/2016, 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},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4356}}
  • T. Bartz-Beielstein, “Forschendes Lernen – vom Bachelor zur Promotion in den Ingenieurwissenschaften,” in Universitas in projects, S. Heuchemer and B. Szczyrba, Eds., {TH Köln}, 2016, pp. 143-170.
    [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] [DOI] C. Jung, M. Zaefferer, T. Bartz-Beielstein, and G. Rudolph, “Metamodel-based optimization of hot rolling processes in the metal industry,” The international journal of advanced manufacturing technology, pp. 1-15, 2016.
    [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 = {2017-10-07 14:36:23 +0000},
    Doi = {10.1007/s00170-016-9386-6},
    Issn = {1433-3015},
    Journal = {The International Journal of Advanced Manufacturing Technology},
    Keywords = {bartzPublic, Bart16n, nonfree},
    Pages = {1--15},
    Title = {Metamodel-based optimization of hot rolling processes in the metal industry},
    Url = {http://dx.doi.org/10.1007/s00170-016-9386-6},
    Year = {2016},
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  • [PDF] T. Bartz-Beielstein, “A Survey of Model-Based Methods for Global Optimization,” in Bioinspired optimization methods and their applications, 2016, pp. 1-18.
    [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 = {2017-03-06 22:24:45 +0000},
    Editor = {Papa, Gregor and Mernik, Marjan},
    Groups = {bartzPublic},
    Keywords = {Surrogate, Bart16n, Bart16e, bartzPublic, free},
    Month = may,
    Pages = {1--18},
    Rating = {0},
    Timestamp = {2016-10-22},
    Title = {{A Survey of Model-Based Methods for Global Optimization}},
    Url = {http://bioma.ijs.si/proceedings/2016/01%20-%20A%20Survey%20of%20Model-Based%20Methods%20for%20Global%20Optimization.pdf},
    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] T. Bartz-Beielstein, “Stacked Generalization of Surrogate Models – A Practical Approach,” TH Köln, Köln, 5/2016, 2016.
    [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 = {2017-10-31 13:07:01 +0000},
    Groups = {bartzPublic},
    Institution = {TH K{\"o}ln},
    Keywords = {Bart16n, Bart16e, bartzPublic, free},
    Note = {\url {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}},
    Url = {urn:nbn:de:hbz:832-cos4-3759},
    Year = {2016},
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  • [PDF] M. A. Rebolledo Coy, S. Krey, T. Bartz-Beielstein, O. Flasch, A. Fischbach, and J. Stork, “Modeling and optimization of a robust gas sensor,” in Bioinspired optimization methods and their applications, 2016, pp. 267-278.
    [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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  • T. Bartz-Beielstein, “Forschendes Lernen – vom Bachelor zur Promotion in den Ingenieurwissenschaften,” in Neues Handbuch Hochschullehre, B. Berendt, A. Fleischmann, N. Schaper, B. Szczyrba, and J. Wildt, Eds., Josef Raabe, 2016, pp. 1-28.
    [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},
    Groups = {bartzPublic},
    Keywords = {bartzPublic, nonfree},
    Pages = {1--28},
    Publisher = {Josef Raabe},
    Rating = {0},
    Title = {{Forschendes Lernen - vom Bachelor zur Promotion in den Ingenieurwissenschaften}},
    Year = {2016},
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  • [PDF] T. Bartz-Beielstein, “Experimental Algorithmics Applied to On-line Machine Learning,” in Bioinspired optimization methods and their applications, 2016, pp. 94-104.
    [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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  • [PDF] M. Friese, T. Bartz-Beielstein, and M. T. M. Emmerich, “Building ensembles of surrogates by optimal convex combination,” in Bioinspired optimization methods and their applications, 2016, pp. 131-144.
    [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] S. Chandrasekaran, S. Moritz, M. Zaefferer, J. Stork, T. Bartz-Beielstein, and T. Bartz-Beielstein, “Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs,” in Workshop computational intelligence, 2016, pp. 1-20.
    [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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  • [PDF] B. Naujoks, J. Stork, M. Zaefferer, and T. Bartz-Beielstein, “Presentation Slides: Meta-Model Assisted Evolutionary Optimization. Tutorial at PPSN 2016,” in Parallel problem solving from nature, 2016, pp. 1-104.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] M. Friese, T. Bartz-Beielstein, and M. Emmerich, “Building Ensembles of Surrogate Models by Optimal Convex Combination (Preprint),” TH Köln, Cologne, 4/2016, 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 = {2017-03-03 10:54:18 +0000},
    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)}},
    Year = {2016},
    Bdsk-File-1 = {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}}
  • [PDF] [DOI] M. Zaefferer, D. Gaida, and T. Bartz-Beielstein, “Multi-fidelity modeling and optimization of biogas plants,” Applied soft computing, vol. 48, pp. 13-28, 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},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1568494616302575},
    Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.asoc.2016.05.047}}
  • [PDF] M. A. Rebolledo Coy, S. Krey, T. Bartz-Beielstein, O. Flasch, A. Fischbach, and J. Stork, “Modeling and Optimization of a Robust Gas Sensor,” Cologne Open Science, Cologne, 03/2016, 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},
    Owner = {bartz},
    Rating = {0},
    Timestamp = {2016-10-26},
    Title = {{Modeling and Optimization of a Robust Gas Sensor}},
    Year = {2016},
    Bdsk-File-1 = {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}}

2015

  • T. Bartz-Beielstein, C. Jung, and M. Zaefferer, “Uncertainty Management Using Sequential Parameter Optimization,” {TH} Köln, Köln, Germany, 4/2015, 2015.
    [Bibtex]
    @techreport{Bart13icos,
    Address = {K{\"o}ln, Germany},
    Author = {Bartz-Beielstein, Thomas and Jung, Christian and Zaefferer, Martin},
    Date-Added = {2017-03-05 15:12:10 +0000},
    Date-Modified = {2017-03-05 15:16:03 +0000},
    Institution = {{TH} K{\"o}ln},
    Keywords = {bartzPublic, free},
    Number = {4/2015},
    Title = {{Uncertainty Management Using Sequential Parameter Optimization}},
    Url = {urn:nbn:de:hbz:832-cos-841},
    Year = {2015},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {urn:nbn:de:hbz:832-cos-841}}
  • [PDF] [DOI] T. Bartz-Beielstein, C. Jung, and M. Zaefferer, “Uncertainty Management Using Sequential Parameter Optimization,” in Uncertainty management in simulation-optimization of complex systems: algorithms and applications, C. Meloni and G. Dellino, Eds., Springer, 2015, pp. 79-99.
    [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}},
    Year = {2015},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-1-4899-7547-8_4}}
  • [PDF] T. Bartz-Beielstein, J. Branke, J. Mehnen, and O. Mersmann, “Overview: Evolutionary Algorithms,” Fakultät 10 / Institut für Informatik, Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, 2/2015, 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},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein and M. Zaefferer, “CIMO – CI-basierte Mehrkriterielle Optimierungsverfahren für Anwendungen in der Industrie (Schlussbericht),” Fachhochschule Köln, Fakultät für Informatik und Ingenieurwissenschaften, TH Köln, 5, 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\"at 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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  • [PDF] T. Bartz-Beielstein, “SPOTSeven Lab: Jahresbericht 2014/15,” 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},
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  • [DOI] T. Bartz-Beielstein, “How to Create Generalizable Results,” in Springer handbook of computational intelligence, J. Kacprzyk and W. Pedrycz, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, pp. 1127-1142.
    [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},
    Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-662-43505-2_56}}
  • [PDF] T. Bartz-Beielstein and M. Zaefferer, “Cimo – ci-basierte mehrkriterielle optimierungsverfahren für anwendungen in der industrie (schlussbericht),” 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 = {2017-01-14 15:34:30 +0000},
    Institution = {Fachhochschule K{\"o}ln, Fakult\"at 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] T. Bartz-Beielstein and M. Zaefferer, “MCIOP – Mehrkriterielle CI-basierte Optimierungsverfahren für den industriellen Einsatz (Schlussbericht),” , 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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  • T. Bartz-Beielstein, “Zen und die Kunst der Hochschullehre,” Changing, iss. 2, pp. 39-49, 2015.
    [Bibtex]
    @article{Bart15p,
    Author = {Bartz-Beielstein, Thomas},
    Date-Added = {2015-11-29T01:34:01GMT},
    Date-Modified = {2017-01-14 15:08:26 +0000},
    Journal = {changing},
    Keywords = {bartzPublic, free},
    Month = sep,
    Number = {2},
    Pages = {39--49},
    Rating = {0},
    Title = {{Zen und die Kunst der Hochschullehre}},
    Year = {2015}}
  • [PDF] T. Bartz-Beielstein, “Meaningful Problem Instances and Generalizable Results,” Cologne University of Applied Science, Betzdorfer Str. 2, 50679 Köln, 1, 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 = {2017-03-25 22:50:19 +0000},
    Institution = {Cologne University of Applied Science},
    Keywords = {bartzPublic, free},
    Month = feb,
    Number = {1},
    Publisher = {{SPOTSeven} Lab, Cologne University of Applied Sciences},
    Rating = {0},
    Read = {Yes},
    Title = {{Meaningful Problem Instances and Generalizable Results}},
    Year = {2015},
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  • [PDF] S. Moritz, A. Sarda, T. Bartz-Beielstein, M. Zaefferer, and J. Stork, “Comparison of different Methods for Univariate Time Series Imputation in R,” , 2015.
    [Bibtex]
    @article{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 = {2017-03-07 09:10:50 +0000},
    Eprint = {1510.03924},
    Eprintclass = {stat.AP},
    Eprinttype = {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}}
  • [PDF] O. Flasch, M. Friese, M. Zaefferer, T. Bartz-Beielstein, and J. Branke, “Learning Model-Ensemble Policies with Genetic Programming,” TH Köln, Köln, 3/2015, 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 = {2017-03-07 22:00:25 +0000},
    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] A. Fischbach, J. Stork, M. Zaefferer, S. Krey, and T. Bartz-Beielstein, “Analyzing Capabilities of Latin Hypercube Designs Compared to Classical Experimental Design Methods,” in Proc. 25. workshop computational intelligence, 2015, pp. 255-269.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein, C. Jung, and M. Zaefferer, “Sequential Parameter Optimization in Noisy Environments,” Cologne University of Applied Sciences, 4, 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}},
    Year = {2015},
    Bdsk-File-1 = {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}}

2014

  • [PDF] [DOI] M. Zaefferer, B. Breiderhoff, B. Naujoks, M. Friese, J. Stork, A. Fischbach, O. Flasch, and T. Bartz-Beielstein, “Tuning multi-objective optimization algorithms for cyclone dust separators,” in Proceedings of the 2014 conference on genetic and evolutionary computation, New York, NY, USA, 2014, pp. 1223-1230.
    [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 simu- lations of cyclone separators are based on time consuming computational fluid dynamics simulations. Thus, the need for exploiting cheap information from analytical, approxima- tive models is evident. Here, we employ two multi-objective optimization algorithms on such cheap, approximative mod- els to analyze their optimization performance on this prob- lem. 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 di↵erent seeds, as well as with a di↵erent approximative model for collection e ciency. Their optimal performance is compared against a model based ap- proach, where multi-objective SPO is directly employed to optimize the Cyclone model, rather than tuning the opti- mization 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 = {2017-03-25 22:50:55 +0000},
    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}}
  • [DOI] T. Bartz-Beielstein, J. Branke, J. Mehnen, and O. Mersmann, “Evolutionary algorithms,” Wiley interdisciplinary reviews: data mining and knowledge discovery, vol. 4, iss. 3, pp. 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},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://dx.doi.org/10.1002/widm.1124}}
  • [PDF] S. Moritz, T. Bartz-Beielstein, O. Mersmann, M. Zaefferer, and J. Stork, “Does imputation work for improvement of domestic hot water usage prediction?,” in Proceedings. 24. workshop computational intelligence, dortmund, 27.-28. november 2014, 2014, pp. 205-222.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein, “SPOTSeven Lab: Forschungsbericht 2013/14,” 2014.
    [Bibtex]
    @techreport{Bart14k,
    Author = {Bartz-Beielstein, Thomas},
    Date-Added = {2015-11-29T01:34:40GMT},
    Date-Modified = {2017-01-14 15:07:08 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {FH K{\"o}ln},
    Rating = {0},
    Title = {{SPOTSeven Lab: Forschungsbericht 2013/14}},
    Year = {2014},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein and M. Preuss, “Experimental Analysis of Optimization Algorithms: Tuning and Beyond,” in Theory and principled methods for designing metaheuristics, Y. Borenstein and A. Moraglio, Eds., Berlin, Heidelberg, New York: Springer, 2014, pp. 205-245.
    [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 = {2017-03-07 21:20:55 +0000},
    Editor = {Borenstein, Yossi and Moraglio, Alberto},
    Keywords = {bartzPublic, nonfree},
    Pages = {205--245},
    Publisher = {Springer},
    Rating = {0},
    Title = {{Experimental Analysis of Optimization Algorithms: Tuning and Beyond}},
    Year = {2014},
    Bdsk-File-1 = {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}}
  • [DOI] Parallel Problem Solving from Nature – PPSN XIII – 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, T. B. Beielstein, J. Branke, B. Filipic, and J. Smith, Eds., Cham: Springer, 2014, vol. 8672.
    [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}}
  • [PDF] J. Stork, A. Fischbach, T. Bartz-Beielstein, and M. Zaefferer, “Boosting Parameter-Tuning Efficiency with Adaptive Experimental Designs,” in Proceedings 24. workshop computational intelligence, 2014, pp. 223-235.
    [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] M. Zaefferer, J. Stork, and T. Bartz-Beielstein, “Distance Measures for Permutations in Combinatorial Efficient Global Optimization,” in Parallel problem solving from nature–ppsn xiii, 2014, pp. 373-383.
    [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] M. Zaefferer, J. Stork, M. Friese, A. Fischbach, B. Naujoks, and T. Bartz-Beielstein, “Efficient Global Optimization for Combinatorial Problems,” in Genetic and evolutionary computation conference (gecco’14), proceedings, 2014, pp. 871-878.
    [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] M. Zaefferer, D. Gaida, and T. Bartz-Beielstein, “Multi-fidelity Simulation and Optimization of a Biogas Plant,” TH Köln, 2/2014, 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 = {2017-03-07 22:36:39 +0000},
    Institution = {TH K{\"o}ln},
    Isbn = {2194-2870},
    Keywords = {bartzPublic, free},
    Number = {2/2014},
    Rating = {0},
    Title = {{Multi-fidelity Simulation and Optimization of a Biogas Plant}},
    Year = {2014},
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2013

  • [PDF] M. Zaefferer, B. Naujoks, and T. Bartz-Beielstein, “A Gentle Introduction to Multi-Criteria Optimization with SPOT,” 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 = {2017-03-03 10:53:31 +0000},
    Institution = {TH K{\"o}ln},
    Keywords = {bartzPublic, free},
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{A Gentle Introduction to Multi-Criteria Optimization with SPOT}},
    Year = {2013},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein and O. Flasch, “FIWA – Methoden der Computational Intelligence für Vorhersagemodelle in der Finanz-und Wasserwirtschaft (Schlussbericht),” , Betzdorfer Str. 2, 50679 Köln 2013.
    [Bibtex]
    @techreport{Bart13m,
    Abstract = {Dieser Schlussbericht beschreibt die im Projekt „Methoden der Computational Intelligence f{\"u}r Vorher- sagemodelle 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 = {2017-03-07 21:35:18 +0000},
    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},
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    Bdsk-Url-1 = {http://opus.bsz-bw.de/fhk/volltexte/2013/46}}
  • [PDF] B. Breiderhoff, T. Bartz-Beielstein, B. Naujoks, M. Zaefferer, A. Fischbach, O. Flasch, M. Friese, O. Mersmann, and J. Stork, “Preprint: Simulation and Optimization of Cyclone Dust Separators,” Fachhochschule Köln, Betzdorfer Str. 2, 50679 Köln, Report 4/2013, 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] [DOI] T. Bartz-Beielstein, M. Zaefferer, and B. Naujoks, “How to create meaningful and generalizable results,” in Proceeding of the fifteenth annual conference companion on genetic and evolutionary computation conference companion, New York, NY, USA, 2013, pp. 979-1004.
    [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] T. Bartz-Beielstein, “SpotSeven Broschüre 2013,” 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},
    Bdsk-File-1 = {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}}
  • [PDF] T. Ludwig, S. Markon, T. Bartz-Beielstein, M. Bongards, and C. Ament, “Power optimization of linear motor elevators using computational intelligence methods,” in JSPS summer program, Hayama, 2013, p. 44.
    [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},
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  • [PDF] [DOI] O. Flasch and T. Bartz-Beielstein, “A Framework for the Empirical Analysis of Genetic Programming System Performance,” in Genetic programming theory and practice x, R. Riolo, E. Vladislavleva, and J. H. Moore, Eds., Ann Arbor, USA: Springer, 2013, pp. 155-170.
    [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] M. Friese, J. Stork, R. R. Guerra, T. Bartz-Beielstein, S. Thaker, O. Flasch, and M. Zaefferer, “UniFIeD Univariate Frequency-based Imputation for Time Series Data,” , 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},
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    Bdsk-Url-1 = {http://opus.bsz-bw.de/fhk/volltexte/2013/49}}
  • [PDF] M. 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 Evolutionary multi-criterion optimization 7th international conference, emo, Heidelberg, 2013, pp. 756-770.
    [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] B. Breiderhoff, T. Bartz-Beielstein, B. Naujoks, M. Zaefferer, A. Fischbach, O. Flasch, M. Friese, O. Mersmann, and J. Stork, “Simulation and Optimization of Cyclone Dust Separators,” in Proceedings 23. workshop computational intelligence, 2013, pp. 177-195.
    [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] T. Bartz-Beielstein, “Mixed Models in SPOT,” TH 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 = {2017-03-07 22:07:13 +0000},
    Institution = {TH K{\"o}ln},
    Keywords = {bartzPublic, free},
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{Mixed Models in SPOT}},
    Year = {2013},
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  • [PDF] [DOI] O. Flasch, M. Friese, K. Vladislavleva, T. Bartz-Beielstein, O. Mersmann, B. Naujoks, J. Stork, and M. Zaefferer, “Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data,” in Applications of evolutionary computation, A. Esparcia-Alcázar, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 172-181.
    [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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2012

  • [DOI] T. Bartz-Beielstein, O. Flasch, and M. Zaefferer, “Sequential parameter optimization for symbolic regression,” in Proceedings of the 14th annual conference companion on genetic and evolutionary computation, New York, NY, USA, 2012, pp. 495-496.
    [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] T. Bartz-Beielstein and M. Zaefferer, “A Gentle Introduction to Sequential Parameter Optimization,” Fakultät 10 / Institut für Informatik, {CIplus} 1/2012, 2012.
    [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 = {2017-03-06 22:16:08 +0000},
    Institution = {Fakult{\"a}t 10 / Institut f{\"u}r Informatik},
    Keywords = {bartzPublic, free, frie17a},
    Number = {1/2012},
    Publisher = {CIplus},
    Rating = {0},
    Title = {{A Gentle Introduction to Sequential Parameter Optimization}},
    Type = {{CIplus}},
    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] T. Bartz-Beielstein, “Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results,” , 3/2012, 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 = {2017-03-06 22:35:21 +0000},
    Keywords = {bartzPublic},
    Month = nov,
    Number = {3/2012},
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results}},
    Year = {2012},
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  • [PDF] O. Flasch and T. Bartz-Beielstein, “Towards a Framework for the Empirical Analysis of Genetic Programming System Performance,” , Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany 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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  • [PDF] M. Zaefferer, T. Bartz-Beielstein, B. Naujoks, T. Wagner, and M. Emmeric, “Model-assisted Multi-criteria Tuning of an Event Detection Software under Limited Budgets,” TH Köln, 2/2012, 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 = {2017-03-07 22:10:20 +0000},
    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 = oct,
    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}},
    Year = {2012},
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  • [PDF] M. Friese, T. Bartz-Beielstein, K. Vladislavleva, O. Flasch, O. Mersmann, B. Naujoks, J. Stork, and M. Zaefferer, “Ensemble-Based Model Selection for Smart Metering Data (Abstract),” 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},
    Bdsk-File-1 = {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}}
  • [PDF] [DOI] M. Zaefferer, T. Bartz-Beielstein, M. Friese, B. Naujoks, and O. Flasch, “Multi-Criteria Optimization for Hard Problems under Limited Budgets,” in Gecco companion ’12: proceedings of the fourteenth international conference on genetic and evolutionary computation conference companion, Philadelphia, Pennsylvania, USA, 2012, pp. 1451-1452.
    [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}},
    Year = {2012},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://dx.doi.org/10.1145/2330784.2330984}}
  • [PDF] M. Friese, T. Bartz-Beielstein, K. Vladislavleva, O. Flasch, O. Mersmann, B. Naujoks, M. Zaefferer, and J. Stork, “Ensemble-Based Model Selection for Smart Metering Data,” in Proceedings 22. workshop computational intelligence, 2012, pp. 215-228.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] [DOI] T. Bartz-Beielstein, M. Preuss, and M. Zaefferer, “Statistical Analysis of Optimization Algorithms with R,” in Gecco 2012 specialized techniques and applications tutorials, Philadelphia, Pennsylvania, USA, 2012, pp. 1259-1286.
    [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},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://dx.doi.org/10.1145/2330784.2330940}}
  • [PDF] [DOI] P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, C. Weihs, and W. Konen, “Tuning and evolution of support vector kernels,” Evolutionary intelligence, vol. 5, iss. 3, pp. 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}}
  • [PDF] M. Zaefferer, B. Naujoks, T. Bartz-Beielstein, M. Friese, O. Mersmann, and O. Flasch, “Mehrkriterielle sequentielle Parameteroptimierung für Anwendungsprobleme mit stark limitiertem Budget,” in Proceedings 22. workshop computational intelligence, 2012, pp. 385-400.
    [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] T. Bartz-Beielstein, “SpotSeven brochure 2012,” , 2012.
    [Bibtex]
    @booklet{Bart12j,
    Author = {Bartz-Beielstein, T},
    Date-Added = {2015-11-29T01:38:28GMT},
    Date-Modified = {2017-01-14 14:58:34 +0000},
    Keywords = {bartzPublic, free},
    Month = feb,
    Rating = {0},
    Title = {{SpotSeven brochure 2012}},
    Year = {2012},
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  • [DOI] G. Ochoa, M. Preuss, T. Bartz-Beielstein, and M. Schoenauer, “Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods,” Evolutionary computation, vol. 20, iss. 2, pp. 161-163, 2012.
    [Bibtex]
    @article{Ocho12a,
    Abstract = {Heuristic search algorithms have been successfully applied to solve many prob- lems in practice. Their design, however, has increased in complexity as the number of parameters and choices for operators and algorithmic components is also expand- ing. There is clearly the need of providing the final user with automated tools to assist the tuning, design and assessment of heuristic optimisation methods. In recent years a growing number workshops and tracks has been held to address these issues. In 2010, the Parallel Problem Solving from Nature (PPSN) conference hosted two workshops, which decided to joint efforts to organise this journal special issue. The workshop 'Self- Tuning, Self-Configuring and Self-Generating Search Heuristics', distinguished three general processes in automated heuristic design: 1) tuning: the process of adjusting the algorithm's control parameters, 2) configuring: the process of selecting and using existing algorithmic components such as search operators, construction heuristics or acceptance criteria, and 3) generating: the process of creating altogether new heuris- tics (or heuristic components) from the basic sub-components of previously existing methods. Machine learning, meta-modelling and multilevel search approaches can and have been applied to automate these three processes. The workshop introduced the term `Self-* Search', which is now the name of a track in GECCO, which started in 2011 and is also being held this year. The other workshop `Methods for the Assessment of Computational Systems' stressed the idea that the experimental analysis of compu- tational systems inspired by nature can be made more sound and effective by the use of appropriate experimental methods. More severe requirements have been transmit- ted to draw objective conclusions from computational experiments, while at the same time the design and configuration of the computational systems can be improved by profitable ways of looking into the data collected.},
    Annote = {doi: 10.1162/EVCO{_}e{_}00071},
    Author = {Ochoa, Gabriela and Preuss, Mike and Bartz-Beielstein, Thomas and Schoenauer, Marc},
    Date-Added = {2015-11-29T01:33:48GMT},
    Date-Modified = {2017-03-07 09:24:16 +0000},
    Doi = {10.1162/EVCO{_}e{_}00071},
    Isbn = {1063-6560},
    Journal = {Evolutionary Computation},
    Keywords = {bartzPublic, nonfree},
    Month = nov,
    Number = {2},
    Pages = {161--163},
    Publisher = {MIT Press},
    Rating = {0},
    Title = {{Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods}},
    Volume = {20},
    Year = {2012},
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  • [PDF] [DOI] T. Bartz-Beielstein, M. Friese, B. Naujoks, and M. Zaefferer, “SPOT Applied to Non-Stochastic Optimization Problems—An Experimental Study,” in Gecco 2012 late breaking abstracts workshop, Philadelphia, Pennsylvania, USA, 2012, pp. 645-646.
    [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] M. Zaefferer, T. Bartz-Beielstein, M. Friese, B. Naujoks, and O. Flasch, “MSPOT: Multi-Criteria Sequential Parameter Optimization,” TH Köln, TR 2/2012, 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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2011

  • [PDF] [DOI] T. Bartz-Beielstein, M. Friese, M. Zaefferer, B. Naujoks, O. Flasch, W. Konen, and P. 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, New York, NY, USA, 2011, pp. 119-120.
    [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},
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  • [PDF] W. Konen, P. Koch, O. Flasch, T. Bartz-Beielstein, M. Friese, and B. Naujoks, “Preprint: Tuned Data Mining: A Benchmark Study on Different Tuners,” TH Köln 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] T. Bartz-Beielstein and M. Zaefferer, “SPOT Package Vignette,” {TH} 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 = {2017-03-08 22:10:49 +0000},
    Institution = {{TH} 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] T. Bartz-Beielstein, M. Friese, O. Flasch, W. Konen, P. Koch, and B. Naujoks, “Ensemble-Based Modeling,” , Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science 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 Scienceand 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 = {2017-03-07 20:54:48 +0000},
    Issn = {2191-365X},
    Keywords = {bartzPublic, free},
    Month = jun,
    Publisher = {Research Center CIOP (Computational Intelligence, Optimization andData Mining)},
    Rating = {0},
    Title = {{Ensemble-Based Modeling}},
    Year = {2011},
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  • [PDF] P. Koch, W. Konen, B. Naujoks, O. Flasch, M. Friese, M. Zaefferer, and T. Bartz-Beielstein, “Tuned Data Mining in R,” in Proceedings 21. workshop computational intelligence, 2011, pp. 147-160.
    [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] O. Flasch, T. Bartz-Beielstein, D. B. 1, W. Kantschik, and C. von Strachwitz, “Results of the GECCO 2011 Industrial Challenge: Optimizing Foreign Exchange Trading Strategies,” , Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science 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 Scienceand 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 = {2017-03-07 23:32:07 +0000},
    Issn = {2191-365X},
    Keywords = {bartzPublic, free},
    Month = dec,
    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},
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  • [PDF] [DOI] W. Konen, P. Koch, O. Flasch, T. Bartz-Beielstein, M. Friese, and B. Naujoks, “Tuned Data Mining: A Benchmark Study on Different Tuners,” in Gecco ’11: proceedings of the 13th annual conference on genetic and evolutionary computation, 2011, pp. 1995-2002.
    [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}}
  • [PDF] P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, and W. Konen, “On the Tuning and Evolution of Support Vector Kernels,” TH Köln, Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science 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 φ 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 Scienceand 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 = {2017-03-07 22:46:48 +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 = {{On the Tuning and Evolution of Support Vector Kernels}},
    Year = {2011},
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  • [PDF] T. Bartz-Beielstein and M. Friese, “Sequential Parameter Optimization and Optimal Computational Budget Allocation for Noisy Optimization Problems,” , Cologne University of Applied Science, Faculty of Computer Science and Engineering Science 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] M. Friese, M. Zaefferer, T. Bartz-Beielstein, O. Flasch, P. Koch, W. Konen, and B. Naujoks, “Ensemble-Based Optimization and Tuning Algorithms,” in Proceedings 21. workshop computational intelligence, 2011, pp. 119-134.
    [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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  • [DOI] T. Bartz-Beielstein and M. Preuss, “Automatic and interactive tuning of algorithms,” in Gecco (companion), New York, New York, USA, 2011, pp. 1361-1380.
    [Bibtex]
    @inproceedings{Bart11g,
    Address = {New York, New York, USA},
    Author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    Booktitle = {GECCO (Companion)},
    Date-Added = {2015-11-29T01:38:08GMT},
    Date-Modified = {2017-01-14 14:55:29 +0000},
    Doi = {10.1145/2001858.2002141},
    Isbn = {9781450306904},
    Keywords = {bartzPublic, BartzTutorial, nonfree},
    Pages = {1361--1380},
    Publisher = {ACM Press},
    Rating = {0},
    Title = {{Automatic and interactive tuning of algorithms}},
    Year = {2011},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/2001858.2002141},
    Bdsk-Url-2 = {http://dx.doi.org/10.1145/2001858.2002141}}

2010

  • [PDF] [DOI] T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, “Introduction—Experimental Methods for the Analysis of Optimization Algorithms,” in Experimental methods for the analysis of optimization algorithms, T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Eds., Berlin, Heidelberg, New York: Springer, 2010, pp. 1-13.
    [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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  • [PDF] P. Koch, W. Konen, O. Flasch, and T. Bartz-Beielstein, “Optimization of Support Vector Regression Models for Stormwater Prediction,” in Proceedings 20. workshop computational intelligence, 2010, pp. 146-160.
    [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},
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  • [PDF] W. Konen, P. Koch, O. Flasch, and T. Bartz-Beielstein, “Parameter-Tuned Data Mining: A General Framework,” in Proceedings 20. workshop computational intelligence, 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] T. Bartz-Beielstein and M. Preuss, “The Future of Experimental Research,” in Experimental methods for the analysis of optimization algorithms, T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Eds., Berlin, Heidelberg, New York: Springer, 2010, pp. 17-46.
    [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 behav- ior. Secondly, the nondeterminism of evolutionary and other metaheuristic methods renders result distributions, not numbers.
    Based on the experience of several tutorials on the matter, we provide a compre- hensive, effective, and very efficient methodology for the design and experimen- tal analysis of metaheuristics such as evolutionary algorithms. We rely on modern statistical techniques for tuning and understanding algorithms from an experimen- tal perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems.},
    Address = {Berlin, Heidelberg, New York},
    Author = {Bartz-Beielstein, Thomas and Preuss, Mike},
    Booktitle = {Experimental Methods for the Analysis of Optimization Algorithms},
    Date-Added = {2015-11-29T01:39:07GMT},
    Date-Modified = {2017-03-08 23:01:05 +0000},
    Editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    Keywords = {bartzPublic, nonfree},
    Pages = {17--46},
    Publisher = {Springer},
    Rating = {0},
    Title = {{The Future of Experimental Research}},
    Year = {2010},
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  • [PDF] T. Bartz-Beielstein, “Sequential Parameter Optimization—An Annotated Bibliography,” , Cologne University of Applied Science, Faculty of Computer Science and Engineering Science 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] T. Bartz-Beielstein, O. Flasch, P. Koch, and W. Konen, “SPOT: A Toolbox for Interactive and Automatic Tuning in the R Environment,” in Proceedings 20. workshop computational intelligence, 2010, pp. 264-273.
    [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] [DOI] T. Bartz-Beielstein, C. Lasarczyk, and M. Preuss, “The Sequential Parameter Optimization Toolbox,” in Experimental methods for the analysis of optimization algorithms, T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Eds., Berlin, Heidelberg, New York: Springer, 2010, pp. 337-360.
    [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] O. Flasch, T. 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 Proc. ieee congress evolutionary computation (cec), 2010, pp. 1579-1586.
    [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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  • [PDF] F. Hutter, T. Bartz-Beielstein, H. Hoos, K. Leyton-Brown, and K. P. Murphy, “Sequential Model-Based Parameter Optimisation: an Experimental Investigation of Automated and Interactive Approaches,” in Experimental methods for the analysis of optimization algorithms, T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Eds., Berlin, Heidelberg, New York: Springer, 2010, pp. 361-414.
    [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 = {2017-03-07 23:36:01 +0000},
    Editor = {Bartz-Beielstein, Thomas and Chiarandini, Marco and Paquete, Luis and Preuss, Mike},
    Keywords = {bartzPublic, nonfree},
    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] O. Flasch, T. Bartz-Beielstein, A. Davtyan, P. Koch, W. Konen, T. D. Oyetoyan, and M. Tamutan, “Comparing CI Methods for Prediction Models in Environmental Engineering,” 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] T. Bartz-Beielstein, “SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization,” , Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, Technical Report (CIOP) , 2010.
    [Bibtex]
    @techreport{Bart10e,
    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 di↵erent 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 = {2017-03-08 22:15:06 +0000},
    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] [DOI] J. Ziegenhirt, T. Bartz-Beielstein, O. Flasch, W. Konen, and M. Zaefferer, “Optimization of Biogas Production with Computational Intelligence—A Comparative Study,” in Proc. 2010 congress on evolutionary computation (cec’10) within ieee world congress on computational intelligence (wcci’10), barcelona, spain, Piscataway NJ, 2010, pp. 3606-3613.
    [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] T. Bartz-Beielstein, M. Preuss, K. Schmitt, and H. Schwefel, “Challenges for Contemporary Evolutionary Algorithms,” 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.},
    Author = {Bartz-Beielstein, Thomas and Preuss, Mike and Schmitt, Karlheinz and Schwefel, Hans--Paul},
    Date-Added = {2015-11-29T01:39:08GMT},
    Date-Modified = {2017-03-06 22:43:26 +0000},
    Keywords = {bartzPublic, free},
    Month = may,
    Publisher = {Faculty of Computer Science, Algorithm Engineering (Ls11), TU Dortmund},
    Rating = {0},
    Title = {{Challenges for Contemporary Evolutionary Algorithms}},
    Year = {2010},
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  • [PDF] T. Bartz-Beielstein, “SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization,” Arxiv e-prints, 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] T. Bartz-Beielstein, “Writing Interfaces for the Sequential Parameter Optimization ToolboxSPOT,” , Cologne University of Applied Science, Faculty of Computer Science and Engineering Science 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 = {2017-01-14 14:51:18 +0000},
    Issn = {2191-365X},
    Keywords = {bartzPublic, free},
    Month = jul,
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{Writing Interfaces for the Sequential Parameter Optimization ToolboxSPOT}},
    Year = {2010},
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  • [PDF] O. Flasch, T. Bartz-Beielstein, A. Davtyan, P. Koch, W. Konen, T. D. Oyetoyan, and M. Tamutan, “Comparing Computational Intelligence Methods for Prediction Models in Environmental Engineering (Preprint),” , 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},
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  • [DOI] T. Bartz-Beielstein and M. Preuss, “Tuning and experimental analysis in evolutionary computation: what we still have wrong,” in Gecco (companion), New York, New York, USA, 2010, pp. 2625-2646.
    [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] T. Bartz-Beielstein, M. Preuss, and H. Schwefel, “Model Optimization with Evolutionary Algorithms,” in Emergence, analysis, and evolution of structures—concepts and strategies across disciplines, K. Lucas and P. Roosen, Eds., Berlin, Heidelberg, New York: Springer, 2010, pp. 47-62.
    [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] T. Bartz-Beielstein, “Performing Experiments Using the Sequential Parameter Optimization Toolbox SPOT,” , Cologne University of Applied Science, Faculty of Computer Science and Engineering Science 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] P. Koch, W. Konen, O. Flasch, and T. Bartz-Beielstein, “Optimizing Support Vector Machines for Stormwater Prediction,” in Proceedings of workshop on experimental methods for the assessment of computational systems joint to ppsn2010, TU Dortmund, 2010, pp. 47-59.
    [Bibtex]
    @inproceedings{Koch10b,
    Abstract = { Inwaterresourcemanagement,e cientcontrollersofstormwa- ter tanks prevent flooding of sewage systems, which reduces environmen- tal 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 combina- tion with consequent parameter tuning using sequential parameter op- timization, 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 e↵ects would lead to de- clined 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 = {2017-03-07 23:03:30 +0000},
    Editor = {Bartz-Beielstein, T and Chiarandini, M and Paquete, L and Preuss, M},
    Keywords = {bartzPublic, free},
    Local-Url = {file://localhost/Users/bartz/Library/Mobile%20Documents/com~apple~CloudDocs/Papers3.d/Papers%20Library/Files/E1/E1354B0B-6BB8-4F88-A570-1815B4E4B99A.pdf},
    Pages = {47--59},
    Rating = {0},
    Title = {{Optimizing Support Vector Machines for Stormwater Prediction}},
    Url = {ls11-www.cs.tu-dortmund.de/_media/techreports/tr10-07.pdf},
    Year = {2010},
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  • [PDF] 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] T. Bartz-Beielstein, “Performing Meta Experiments Using the Sequential Parameter Optimization Toolbox SPOT,” , 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 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 di↵erent 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 = {2017-03-07 23:22:41 +0000},
    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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  • [PDF] O. Flasch, O. Mersmann, and T. Bartz-Beielstein, “RGP: An Open Source Genetic Programming System for the R Environment,” in Genetic and evolutionary computation conference, gecco 2010, proceedings, portland, oregon, 2010, pp. 2071-2072.
    [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] T. Bartz-Beielstein, O. Flasch, P. Koch, and W. Konen, “SPOT: A Toolbox for Interactive and Automatic Tuning of Search Heuristics and Simulation Models in the R Environment,” 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] J. Ziegenhirt, T. Bartz-Beielstein, O. Flasch, W. Konen, and M. Zaefferer, “Optimization of Biogas Production with Computational Intelligence – A Comparative Study (Preprint),” 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},
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  • [PDF] O. Flasch, T. Bartz-Beielstein, P. Koch, and W. Konen, “Clustering Based Niching for Genetic Programming in the R Environment,” in Proceedings 20. workshop computational intelligence, 2010, pp. 33-46.
    [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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  • [DOI] Experimental Methods for the Analysis of Optimization Algorithms, T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Eds., Berlin, Heidelberg, New York: Springer, 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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2009

  • [DOI] T. Bartz-Beielstein and M. Preuss, “The future of experimental research (tutorial),” in Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers, New York, NY, USA, 2009, pp. 3185-3226.
    [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},
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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}}
  • [DOI] W. Konen and T. 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, New York, NY, USA, 2009, pp. 2641-2648.
    [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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  • [PDF] O. Flasch and T. Bartz-Beielstein, “Sequential parameter optimization applied to evolutionary strategies for portfolio optimization,” in Second workshop of the ercim working group on computing and statistics (ercim 09), 2009, p. 107.
    [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},
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  • [DOI] W. Konen, T. Zimmer, and T. Bartz-Beielstein, “Optimized Modelling of Fill Levels in Stormwater Tanks Using CI-based Parameter Selection Schemes (in german),” At-automatisierungstechnik, vol. 57, iss. 3, pp. 155-166, 2009.
    [Bibtex]
    @article{Kone09b,
    Abstract = {Ziel dieses Beitrags ist die Prognose der Fu ̈llst ̈ande in Regenu ̈berlaufbecken aufgrund von Regeneintrag und Bodenbescha↵enheit. Wir vergleichen verschiedene Prognoseverfahren und nutzen die Sequentielle Parameteroptimierung (SPO), um fu ̈r jedes Verfahren in vergleichbarer Weise bestmo ̈gliche Parameter zu finden. Es zeigt sich, dass diverse Standard- und CI-basierte Verfahren der Modellierung mit intermittierenden Regenme{\ss}daten als Input nicht gut zurecht kommen. Problemspezifische Modellierungen, die kausale E↵ekte erster Ordnung beru ̈cksichtigen, erzielen wesentlich kleinere Prognosefehler. Wichtige Resultate dieser Arbeit sind: (i) SPO l ̈asst sich auf verschiedene Modellierungsverfahren gleicherma{\ss}en anwenden und automatisiert das manuell zeitaufwendige Parameter-Tuning, (ii) das beste manuell erzielte Ergebnis wurde mit SPO nochmals um ca. 30% verbessert und (iii) SPO analysiert in konsistenter Weise den Einflu{\ss} 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 di↵erent 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 behaviour. 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 automates 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 parameter 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 = {2017-03-09 21:05:05 +0000},
    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}}
  • [PDF] R. Stoean, T. Bartz-Beielstein, M. Preuss, and C. Stoean, “A Support Vector Machine-Inspired Evolutionary Approach for Parameter Setting in Metaheuristics,” 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},
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  • [PDF] C. Stoean, M. Preuss, T. Bartz-Beielstein, and R. Stoean, “A New Clustering-Based Evolutionary Algorithm for Real-Valued Multimodal Optimization,” , 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},
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  • [PDF] T. Bartz-Beielstein, “Sequential Parameter Optimization,” in Sampling-based optimization in the presence of uncertainty, Dagstuhl, Germany, 2009, pp. 1-32.
    [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},
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    Bdsk-Url-1 = {http://drops.dagstuhl.de/opus/volltexte/2009/2115}}
  • [PDF] O. Flasch, T. Bartz-Beielstein, P. Koch, and W. Konen, “Genetic Programming Applied to Predictive Control in Environmental Engineering,” in Proceedings 19. workshop computational intelligence, Karlsruhe, 2009, pp. 101-113.
    [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},
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2008

  • [DOI] T. Bartz-Beielstein and M. Preuss, “Experimental research in evolutionary computation–The Future of Experimental Research ( GECCO Tutorial 2008),” in Gecco (companion), New York, New York, USA, 2008, pp. 2517-2534.
    [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] T. Bartz-Beielstein, T. Zimmer, and W. Konen, “Parameterselektion für komplexe Modellierungsaufgaben der Wasserwirtschaft — Moderne CI-Verfahren zur Zeitreihenanalyse,” in Proc. 18th workshop computational intelligence, 2008, pp. 136-150.
    [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},
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  • [PDF] T. Bartz-Beielstein, C. Lasarczyk, and M. Preuss, “SPOT: Sequential Parameter Optimization Toolbox,” 2008.
    [Bibtex]
    @techreport{Bart06f,
    Author = {Bartz-Beielstein, Thomas and Lasarczyk, Christian and Preuss, Mike},
    Date-Added = {2015-11-29T01:38:47GMT},
    Date-Modified = {2017-10-24 10:58:59 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {Universit{\"a}t Dortmund, Germany},
    Rating = {0},
    Title = {{SPOT: Sequential Parameter Optimization Toolbox}},
    Year = {2008},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://sfbci.uni-dortmund.de/Publications/Reference/Downloads/25608.pdf}}
  • [PDF] T. Bartz-Beielstein and W. Konen, “Moderne statistische Verfahren zur Parameteroptimierung und systematischen Modellauswahl,” 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 = {2017-03-07 22:18:01 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{Moderne statistische Verfahren zur Parameteroptimierung und systematischen Modellauswahl}},
    Year = {2008},
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  • [PDF] [DOI] W. Konen and T. Bartz-Beielstein, “Reinforcement Learning: Insights from Interesting Failures in Parameter Selection,” in Ppsn’2008: 10th international conference on parallel problem solving from nature, dortmund, Berlin, 2008, pp. 478-487.
    [Bibtex]
    @inproceedings{Kone08a,
    Abstract = {We investigate reinforcement learning methods, namely the temporal difference learning TD( ) 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 di↵erences 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 = {2017-03-07 23:31:44 +0000},
    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},
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    Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-540-87700-4_48}}
  • [PDF] W. Konen and T. Bartz-Beielstein, “Reinforcement Learning für strategische Brettspiele,” 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, W and Bartz-Beielstein, T},
    Date-Added = {2015-11-29T01:41:08GMT},
    Date-Modified = {2017-03-07 23:30:17 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {FH K{\"o}ln},
    Rating = {0},
    Title = {{Reinforcement Learning f{\"u}r strategische Brettspiele}},
    Year = {2008},
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  • [PDF] T. Bartz-Beielstein and W. Konen, “Datenanalyse und Prozessoptimierung am Beispiel Kläranlagen,” 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 = {2017-03-07 09:16:44 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{Datenanalyse und Prozessoptimierung am Beispiel Kl{\"a}ranlagen}},
    Year = {2008},
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  • [PDF] T. Bartz-Beielstein, C. Lasarczyk, and M. Preuss, “SPOT – Sequential Parameter Optimization Toolbox (MATLAB Documentation),” , Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science 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 Scienceand Engineering Science},
    Author = {Bartz-Beielstein, Thomas and Lasarczyk, Christian and Preuss, Mike},
    Date-Added = {2015-11-29T01:38:58GMT},
    Date-Modified = {2017-03-08 22:18:59 +0000},
    Issn = {2191-365X},
    Keywords = {bartzPublic, free},
    Month = jan,
    Publisher = {Research Center CIOP (Computational Intelligence, Optimization andData Mining)},
    Rating = {0},
    Title = {{SPOT - Sequential Parameter Optimization Toolbox (MATLAB Documentation)}},
    Year = {2008},
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  • [PDF] T. Bartz-Beielstein and M. Preuss, Preprint: The Future of Experimental Research (Tutorial)Cologne University of Applied Science, Faculty of Computer Science and Engineering Science: Research Center CIOP (Computational Intelligence, Optimization and Data Mining), 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},
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  • [PDF] T. Bartz-Beielstein and W. Konen, “Genetisches Programmieren für Vorhersagemodelle in der Finanzwirtschaft,” 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 = {2017-03-07 21:42:17 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {Cologne University of Applied Sciences},
    Rating = {0},
    Title = {{Genetisches Programmieren f{\"u}r Vorhersagemodelle in der Finanzwirtschaft}},
    Year = {2008},
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  • [PDF] W. Konen and T. Bartz-Beielstein, “Internationaler DATA-MINING-CUP (DMC) mit studentischer Beteiligung des Campus Gummersbach,” 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, W and Bartz-Beielstein, T},
    Date-Added = {2015-11-29T01:41:05GMT},
    Date-Modified = {2017-03-07 21:53:51 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {FH K{\"o}ln},
    Rating = {0},
    Title = {{Internationaler DATA-MINING-CUP (DMC) mit studentischer Beteiligung des Campus Gummersbach}},
    Year = {2008},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein, “Review: Design and Analysis of Simulation Experiments by Jack P.C. Kleijnen,” Informs computing society news, vol. 2, pp. 11-14, 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] [DOI] T. Bartz-Beielstein, “How Experimental Algorithmics Can Benefit from Mayo’s Extensions to Neyman-Pearson Theory of Testing,” Synthese, vol. 163, iss. 3, pp. 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 optimiza- tion problems, e.g., problems from chemical engineering, airfoil optimization, or bio- informatics, 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 = {2017-03-07 21:48:10 +0000},
    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},
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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}}

2007

  • [DOI] Hybrid metaheuristics, 4th international workshop, HM 2007, dortmund, germany, october 8-9, 2007, proceedingsSpringer, 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 = {2017-03-03 11:25:05 +0000},
    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] T. Bartz-Beielstein, W. Konen, and Westenberger, “Computational Intelligence und Data Mining — Moderne statistische Verfahren zur experimentellen Versuchsplanung,” 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, T and Konen, W and {Westenberger}},
    Date-Added = {2015-11-29T01:38:47GMT},
    Date-Modified = {2017-03-07 09:13:23 +0000},
    Keywords = {bartzPublic, free},
    Publisher = {FH K{\"o}ln},
    Rating = {0},
    Title = {{Computational Intelligence und Data Mining -- Moderne statistische Verfahren zur experimentellen Versuchsplanung}},
    Year = {2007},
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  • [DOI] T. Bartz-Beielstein and M. Preuss, “Experimental research in evolutionary computation (GECCO 2007),” in Proceedings of the 2007 gecco conference companion on genetic and evolutionary computation, New York, NY, USA, 2007, pp. 3001-3020.
    [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},
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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] T. Bartz-Beielstein, M. Bongards, C. Claes, W. Konen, and H. Westenberger, “Datenanalyse und Prozessoptimierung für Kanalnetze und Kläranlagen mit CI-Methoden,” in Proc. 17th workshop computational intelligence, 2007, pp. 132-138.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] H. Westenberger, W. Konen, and T. Bartz-Beielstein, “Computational Intelligence und Data Mining — Business Intelligence an Hochschulen,” 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 Leis- tungsprozesse mittels geeigneter Ma{\ss}nahmen zielorientiert optimieren. Der „Gesch{\"a}ftser- folg`` 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 wer- den bereits heute aus den operativen Datenquellen der Hochschulverwaltungssysteme ab- geleitet. 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 er- weitern helfen. Damit stellt BI eine optimierte Grundlage f{\"u}r effektive Entscheidungsprozesse bereit.},
    Author = {Westenberger, H and Konen, W and Bartz-Beielstein, T},
    Date-Added = {2015-11-29T01:43:38GMT},
    Date-Modified = {2017-03-07 09:11:52 +0000},
    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}},
    Year = {2007},
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  • [PDF] J. Mehnen, T. Michelitsch, C. Lasarczyk, and T. Bartz-Beielstein, “Multi-objective evolutionary design of mold temperature control using DACE for parameter optimization,” International journal of applied electromagnetics and mechanics, vol. 25, iss. 1–4, pp. 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},
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    Bdsk-Url-1 = {http://iospress.metapress.com/content/751K5GG10P79Q501}}
  • [PDF] W. Konen, T. Bartz-Beielstein, and H. Westenberger, “Computational Intelligence und Data Mining — Datenanalyse und Prozessoptimierung am Beispiel Kläranlagen,” 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, W and Bartz-Beielstein, T and Westenberger, H},
    Date-Added = {2015-11-29T01:41:06GMT},
    Date-Modified = {2017-03-07 09:12:29 +0000},
    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}},
    Year = {2007},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein, W. Konen, and Westenberger, “Computational Intelligence und Data Mining — Portfoliooptimierung unter Nebenbedingungen,” 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, T and Konen, W and {Westenberger}},
    Date-Added = {2015-11-29T01:38:47GMT},
    Date-Modified = {2017-03-07 09:13:57 +0000},
    Institution = {FH K{\"o}ln},
    Keywords = {bartzPublic, free},
    Publisher = {FH K{\"o}ln},
    Rating = {0},
    Title = {{Computational Intelligence und Data Mining -- Portfoliooptimierung unter Nebenbedingungen}},
    Year = {2007},
    Bdsk-File-1 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YVtiYXJ0MDdjLnBkZtIXCxgZV05TLmRhdGFPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LYmFydDA3Yy5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAIAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpiYXJ0MDdjLnBkZgAADgAYAAsAYgBhAHIAdAAwADcAYwAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL2JhcnQwN2MucGRmAAATAAEvAAAVAAIADP//AACABtIbHB0eWiRjbGFzc25hbWVYJGNsYXNzZXNdTlNNdXRhYmxlRGF0YaMdHyBWTlNEYXRhWE5TT2JqZWN00hscIiNcTlNEaWN0aW9uYXJ5oiIgXxAPTlNLZXllZEFyY2hpdmVy0SYnVHJvb3SAAQAIABEAGgAjAC0AMgA3AEAARgBNAFUAYABnAGoAbABuAHEAcwB1AHcAhACOAJoAnwCnAfcB+QH+AgkCEgIgAiQCKwI0AjkCRgJJAlsCXgJjAAAAAAAAAgEAAAAAAAAAKAAAAAAAAAAAAAAAAAAAAmU=}}
  • [PDF] M. Preuss and T. Bartz-Beielstein, “Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms,” in Parameter setting in evolutionary algorithms, F. Lobo, C. Lima, and Z. Michalewicz, Eds., Berlin, Heidelberg, New York: Springer, 2007, pp. 91-120.
    [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 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YVtwcmV1MDdhLnBkZtIXCxgZV05TLmRhdGFPEQFMAAAAAAFMAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8LcHJldTA3YS5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAIAAAogY3UAAAAAAAAAAAAAAAAACldlYnN0b3JlLmQAAgArLzpVc2VyczpiYXJ0ejpzY2llYm86V2Vic3RvcmUuZDpwcmV1MDdhLnBkZgAADgAYAAsAcAByAGUAdQAwADcAYQAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAKVVzZXJzL2JhcnR6L3NjaWViby9XZWJzdG9yZS5kL3ByZXUwN2EucGRmAAATAAEvAAAVAAIADP//AACABtIbHB0eWiRjbGFzc25hbWVYJGNsYXNzZXNdTlNNdXRhYmxlRGF0YaMdHyBWTlNEYXRhWE5TT2JqZWN00hscIiNcTlNEaWN0aW9uYXJ5oiIgXxAPTlNLZXllZEFyY2hpdmVy0SYnVHJvb3SAAQAIABEAGgAjAC0AMgA3AEAARgBNAFUAYABnAGoAbABuAHEAcwB1AHcAhACOAJoAnwCnAfcB+QH+AgkCEgIgAiQCKwI0AjkCRgJJAlsCXgJjAAAAAAAAAgEAAAAAAAAAKAAAAAAAAAAAAAAAAAAAAmU=}}
  • [PDF] T. Bartz-Beielstein, D. Blum, and J. Branke, “Particle Swarm Optimization and Sequential Sampling in Noisy Environments,” in Metaheuristics–progress in complex systems optimization, K. F. Doerner and others, Eds., Berlin, Heidelberg, New York: Springer, 2007, pp. 261-273.
    [Bibtex]
    @incollection{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 stagna- tion 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 pro- cedure, called optimal computing budget allocation, which attempts to distribute a given number of samples in the most effective way. Exper- imental results show that this new algorithm indeed outperforms the other alternatives.},
    Address = {Berlin, Heidelberg, New York},
    Author = {Bartz-Beielstein, Thomas and Blum, Daniel and Branke, J{\"u}rgen},
    Booktitle = {Metaheuristics--Progress in Complex Systems Optimization},
    Date-Added = {2015-11-29T01:38:41GMT},
    Date-Modified = {2017-03-07 23:11:28 +0000},
    Editor = {Doerner, Karl F and others},
    Keywords = {bartzPublic, free},
    Pages = {261--273},
    Publisher = {Springer},
    Rating = {0},
    Title = {{Particle Swarm Optimization and Sequential Sampling in Noisy Environments}},
    Year = {2007},
    Bdsk-File-1 = {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}}

2006

  • [PDF] B. Baranski, T. Bartz-Beielstein, R. Ehlers, T. Kajendran, B. Kosslers, J. Mehnen, T. Polazek, R. Reimholz, J. Schmidt, K. Schmitt, D. Seis, R. Slodzinski, S. Steeg, N. Wiemann, and M. Zimmermann, “High-order punishment and the evolution of cooperation,” in Proc. genetic and evolutionary computation conf. (gecco 2006), seattle wa, New York, 2006, pp. 379-380.
    [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 = {2016-11-13 14:26:07 +0000},
    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},
    Bdsk-File-1 = {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}}
  • [PDF] B. Naujoks, D. Quagliarella, and T. Bartz-Beielstein, “Sequential parameter optimisation of evolutionary algorithms for airfoil design,” in Proc. design and optimization: methods and applications, (ercoftac’06), 2006, pp. 231-235.
    [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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  • [PDF] B. Baranski, T. Bartz-Beielstein, R. Ehlers, T. Kajendran, B. Kosslers, J. Mehnen, T. Polazek, R. Reimholz, J. Schmidt, K. Schmitt, D. Seis, R. Slodzinski, S. Steeg, N. Wiemann, and M. 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 repre- sentations: 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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  • [DOI] Control of Traffic Systems in Buildings, S. Markon, H. Kita, H. Kise, and T. Bartz-Beielstein, Eds., Berlin, Heidelberg, New York: Springer, 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},
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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] T. Bartz-Beielstein and M. Preuss, “Moderne Methoden zur experimentellen Analyse evolutionärer Verfahren,” in Proc. 16th workshop computational intelligence, 2006, pp. 25-32.
    [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] [DOI] B. Baranski, T. Bartz-Beielstein, R. Ehlers, T. Kajendran, B. Kosslers, J. Mehnen, T. Polazek, R. Reimholz, J. Schmidt, K. Schmitt, D. Seis, R. Slodzinski, S. Steeg, N. Wiemann, and M. Zimmermann, “The impact of group reputation in multiagent environments,” in Proc. 2006 congress on evolutionary computation (cec’06) within fourth ieee world congress on computational intelligence (wcci’06), vancouver bc, Piscataway NJ, 2006, pp. 1224-1231.
    [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] T. Bartz-Beielstein and M. Preuss, “Considerations of Budget Allocation for Sequential Parameter Optimization (SPO),” in Workshop on empirical methods for the analysis of algorithms, proceedings, Reykjavik, Iceland, 2006, pp. 35-40.
    [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}}
  • [PDF] T. Bartz-Beielstein, “Neyman-Pearson Theory of Testing and Mayo’s Extensions in Evolutionary Computing (preprint),” 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 mathe- matical 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 ex- perimental research in EC (before 1980), which can be characterized as ``foundation and development,'' the comparison of di↵erent 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 imple- mentable 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 = {2017-03-07 22:44:14 +0000},
    Keywords = {bartzPublic, free},
    Rating = {0},
    Title = {{Neyman-Pearson Theory of Testing and Mayo's Extensions in Evolutionary Computing} (preprint)},
    Url = {http://www.error06.econ.vt.edu/bartzerror2006.pdf},
    Year = {2006},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://www.error06.econ.vt.edu/bartzerror2006.pdf}}
  • [PDF] T. Bartz-Beielstein, “SPOT—A Toolbox for Visionary Ideas,” in Hans–paul schwefel—festschrift, T. Bartz-Beielstein, G. Jankord, B. Naujoks, and others, Eds., Dortmund, Germany: Dortmund University, Chair of Systems Analysis, 2006, pp. 21-26.
    [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] Hans–Paul Schwefel—Festschrift, T. Bartz-Beielstein, G. Jankord, B. Naujoks, and others, Eds., Dortmund, Germany: Dortmund University, Chair of Systems Analysis, 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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}}
  • [DOI] T. Bartz-Beielstein, Experimental Research in Evolutionary Computation—The New Experimentalism, Berlin, Heidelberg, New York: Springer, 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 = {2017-03-07 21:32:14 +0000},
    Doi = {10.1007/3-540-32027-X},
    Groups = {bartzPublic},
    Isbn = {3-540-32026-1},
    Keywords = {bartzPublic, nonfree},
    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},
    Bdsk-File-1 = {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},
    Bdsk-Url-1 = {http://dx.doi.org/10.1007/3-540-32027-X}}
  • [PDF] [DOI] T. Bartz-Beielstein, M. Preuss, and G. Rudolph, “Investigation of One-Go Evolution Strategy/Quasi-Newton Hybridizations,” in Proceedings third international workshop hybrid metaheuristics (hm’06), Berlin, Heidelberg, New York, 2006, pp. 178-191.
    [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] [DOI] T. Bartz-Beielstein, A. Chmielewski, M. Janas, B. Naujoks, and R. Scheffermann, “Optimizing door assignment in LTL-terminals by evolutionary multiobjective algorithms,” in Proc. 2006 congress on evolutionary computation (cec’06) within fourth ieee world congress on computational intelligence (wcci’06), vancouver bc, Piscataway NJ, 2006, pp. 348-354.
    [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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    Bdsk-Url-1 = {http://dx.doi.org/10.1109/CEC.2006.1688288}}

2005

  • [PDF] [DOI] T. Bartz-Beielstein, C. Lasarczyk, and M. Preuss, “Sequential Parameter Optimization,” in Proceedings 2005 congress on evolutionary computation (cec’05), edinburgh, scotland, Piscataway NJ, 2005, pp. 773-780.
    [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 = {2017-03-03 22:54:58 +0000},
    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},
    Language = {English},
    Pages = {773--780},
    Publisher = {IEEE Press},
    Rating = {0},
    Read = {Yes},
    Title = {{Sequential Parameter Optimization}},
    Year = {2005},
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    Bdsk-Url-1 = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1554761},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/CEC.2005.1554761}}
  • [PDF] T. Bartz-Beielstein, D. Blum, and J. Branke, “Particle Swarm Optimization and Sequential Sampling in Noisy Environments,” in Proceedings 6th metaheuristics international conference (mic2005), Vienna, Austria, 2005, pp. 89-94.
    [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},
    Bdsk-File-1 = {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}}
  • [PDF] T. Bartz-Beielstein, “Evolution Strategies and Threshold Selection,” in Proceedings second international workshop hybrid metaheuristics (hm’05), Berlin, Heidelberg, New York, 2005, pp. 104-115.
    [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] T. Bartz-Beielstein, “New Experimentalism Applied to Evolutionary Computation,” PhD Thesis, 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-2 = {http://dx.doi.org/10.17877/DE290R-15667}}
  • [PDF] [DOI] T. Bartz-Beielstein, M. Preuss, and S. Markon, “Validation and optimization of an elevator simulation model with modern search heuristics,” in Metaheuristics: progress as real problem solvers, T. Ibaraki, K. Nonobe, and M. Yagiura, Eds., Berlin, Heidelberg, New York: Springer, 2005, pp. 109-128.
    [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 = {2017-03-08 23:41:58 +0000},
    Doi = {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}},
    Url = {http://dx.doi.org/10.1007/0-387-25383-1_5},
    Year = {2005},
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    Bdsk-Url-1 = {http://dx.doi.org/10.1007/0-387-25383-1_5}}
  • J. Mehnen, T. Michelitsch, C. W. G. Lasarczyk, and T. Bartz-Beielstein, “Multiobjective Evolutionary Design of Mold Temperature Control using DACE for Parameter Optimization,” in Proceedings twelfth international symposium interdisciplinary electromagnetics, mechanics, and biomedical problems (isem 2005), Vienna, Austria, 2005, pp. 464-465.
    [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] T. Bartz-Beielstein, K. E. Parsopoulos, and M. N. Vrahatis, “Design and analysis of optimization algorithms using computational statistics,” Applied numerical analysis and computational mathematics (anacm), vol. 1, iss. 2, pp. 413-433, 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 = {2017-03-07 09:17:51 +0000},
    Groups = {bart16n},
    Journal = {Applied Numerical Analysis and Computational Mathematics (ANACM)},
    Keywords = {Bart16n, bartzPublic},
    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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  • K. Weinert, J. Mehnen, T. Michelitsch, K. Schmitt, and T. 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, vol. XI, iss. 1, pp. 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}}
  • [PDF] T. Bartz-Beielstein, K. E. Parsopoulos, and M. N. Vrahatis, “Analysis of Particle Swarm Optimization Using Computational Statistics,” in Proceedings international conference numerical analysis and applied mathematics (icnaam), Weinheim, Germany, 2004, pp. 34-37.
    [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] T. Bartz-Beielstein, M. de Vegt, K. E. Parsopoulos, and M. N. Vrahatis, “Designing Particle Swarm Optimization with Regression Trees,” 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 = {2017-03-07 09:18:54 +0000},
    Doi = {10.17877/DE290R-5432},
    Keywords = {bartzPublic, free},
    Month = Mai,
    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] J. Mehnen, T. Michelitsch, T. Bartz-Beielstein, and N. Henkenjohann, “Systematic analyses of multi-objective evolutionary algorithms applied to real-world problems using statistical design of experiments,” in Proceedings fourth international seminar intelligent computation in manufacturing engineering, Naples, Italy, 2004, pp. 171-178.
    [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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  • [PDF] T. Bartz-Beielstein, K. Schmitt, J. Mehnen, B. Naujoks, and D. Zibold, “KEA—A Software Package for Development, Analysis, and Application of Multiple Objective Evolutionary Algorithms,” Universität Dortmund, Germany, CI-185/04, 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},
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  • [DOI] J. Mehnen, T. Michelitsch, T. Bartz-Beielstein, and K. 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), vol. 218, iss. B6, pp. 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-2 = {http://pib.sagepub.com/content/218/6/657.full.pdf}}
  • [PDF] T. Bartz-Beielstein and B. Naujoks, “Tuning Multicriteria Evolutionary Algorithms for Airfoil Design Optimization,” Universität Dortmund, Germany, CI-159/04, 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},
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  • [PDF] [DOI] T. Bartz-Beielstein and S. Markon, “Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach,” in Proceedings 2004 congress on evolutionary computation (cec’04), portland or, Piscataway NJ, 2004, pp. 1111-1118.
    [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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2003

  • [PDF] T. Bartz-Beielstein, “Experimental Analysis of Evolution Strategies—Overview and Comprehensive Introduction,” University Dortmund, Reihe {CI}. {SFB} 531 157/03, 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 = {2017-03-07 21:20:12 +0000},
    Institution = {University Dortmund},
    Keywords = {bartzPublic, Bart16n, free},
    Month = nov,
    Number = {157/03},
    Rating = {0},
    Title = {{Experimental Analysis of Evolution Strategies---Overview and Comprehensive Introduction}},
    Type = {Reihe {CI}. {SFB} 531},
    Year = {2003},
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  • [PDF] T. Bartz-Beielstein, P. Limbourg, J. Mehnen, K. Schmitt, K. E. Parsopoulos, and M. N. Vrahatis, “Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques – Preprint,” Universität Dortmund, Germany 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},
    Date-Added = {2015-11-29T01:38:59GMT},
    Date-Modified = {2016-11-15 13:34:54 +0000},
    Institution = {Universit{\"a}t Dortmund, Germany},
    Keywords = {bartzPublic, free},
    Month = 7,
    Rating = {0},
    Title = {{Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques - Preprint}},
    Year = {2003},
    Bdsk-File-1 = {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}}
  • T. Bartz-Beielstein, M. Preuss, and A. Reinholz, “Evolutionary algorithms for optimization practitioners (Tutorial),” in Proceedings 5th metaheuristics international conference (mic’03) kyoto, japan, 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 = {2017-01-14 15:13:38 +0000},
    Keywords = {bartzPublic, tutorial, BartzTutorial, free},
    Month = 8,
    Rating = {0},
    Title = {{Evolutionary algorithms for optimization practitioners (Tutorial)}},
    Year = {2003}}
  • [PDF] T. Beielstein, M. Preuss, and S. Markon, “A Parallel Approach to Elevator Optimization,” 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},
    Date-Added = {2015-11-29T01:39:36GMT},
    Date-Modified = {2017-03-06 22:21:01 +0000},
    Institution = {Technische Universit\"at Dortmund},
    Keywords = {bartzPublic, free},
    Month = nov,
    Publisher = {Universit{\"a}t Dortmund, Germany},
    Rating = {0},
    Title = {{A Parallel Approach to Elevator Optimization}},
    Year = {2003},
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  • [PDF] T. Bartz-Beielstein, M. Preuss, and S. Markon, “Validation and optimization of an elevator simulation model with modern search heuristics,” Universität Dortmund, Germany 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},
    Date-Modified = {2017-03-08 23:42:15 +0000},
    Institution = {Universit{\"a}t Dortmund, Germany},
    Keywords = {bartzPublic, free},
    Month = 12,
    Rating = {0},
    Title = {{Validation and optimization of an elevator simulation model with modern search heuristics}},
    Year = {2003},
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  • [PDF] T. Beielstein, S. Markon, and M. Preuss, “A Parallel Approach to Elevator Optimization Based on Soft Computing,” in Proceedings 5th metaheuristics international conference (mic’03), Kyoto, Japan, 2003, p. 07/1–07/11 (CD–ROM).
    [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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    Pages = {07/1--07/11 (CD--ROM)},
    Rating = {0},
    Title = {{A Parallel Approach to Elevator Optimization Based on Soft Computing}},
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  • [DOI] T. Beielstein, J. Mehnen, L. Schönemann, H. Schwefel, T. Surmann, K. Weinert, and D. Wiesmann, “Design of evolutionary algorithms and applications in surface re\-con\-struc\-tion,” in Advances in computational intelligence—theory and practice, H. P. Schwefel, I. Wegener, and K. Weinert, Eds., Berlin, Heidelberg, New York: Springer, 2003, pp. 145-193.
    [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 = {2017-01-14 15:52:32 +0000},
    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},
    Keywords = {bartzPublic, nonfree},
    Pages = {145--193},
    Publisher = {Springer},
    Rating = {0},
    Title = {{Design of evolutionary algorithms and applications in surface re\-con\-struc\-tion}},
    Uri = {\url{papers3://publication/doi/10.1007/978-3-662-05609-7_6}},
    Url = {http://dx.doi.org/10.1007/978-3-662-05609-7_6},
    Year = {2003},
    Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-662-05609-7_6}}
  • [PDF] T. Beielstein, M. Preuss, and S. Markon, “Algorithm based validation of a simplified elevator group controller model,” 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, T and Preuss, M and Markon, S},
    Date-Added = {2015-11-29T01:39:35GMT},
    Date-Modified = {2017-03-06 22:30:23 +0000},
    Institution = {Technische Universit\"at Dortmund},
    Keywords = {bartzPublic, free},
    Publisher = {Universit{\"a}t Dortmund, Germany},
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    Title = {{Algorithm based validation of a simplified elevator group controller model}},
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  • [PDF] T. Bartz-Beielstein, S. Markon, and M. Preuss, “Algorithm Based Validation of a Simplified Elevator Group Controller Model,” in Proceedings 5th metaheuristics international conference (mic’03), Kyoto, Japan, 2003, p. 06/1–06/13 (CD–ROM).
    [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},
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    Title = {{Algorithm Based Validation of a Simplified Elevator Group Controller Model}},
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  • [DOI] T. Bartz-Beielstein, J. Mehnen, K. Schmitt, K. E. Parsopoulos, and M. N. Vrahatis, “Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques,” in Proceedings 2003 congress on evolutionary computation (cec’03), canberra, Piscataway NJ, 2003, pp. 1780-1787.
    [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 = {2016-11-15 13:33:56 +0000},
    Doi = {10.1109/CEC.2003.1299888},
    Editor = {Sarker, R and others},
    Isbn = {0-7803-7804-0},
    Keywords = {bartzPublic, free},
    Month = {Dec},
    Pages = {1780--1787},
    Publisher = {IEEE},
    Rating = {0},
    Title = {{Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques}},
    Url = {http://dx.doi.org/10.1109/CEC.2003.1299888},
    Volume = {3},
    Year = {2003},
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  • [PDF] [DOI] T. Beielstein, “Tuning Evolutionary Algorithms—Overview and Comprehensive Introduction,” Technische Universität Dortmund 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}},
    Url = {https://eldorado.tu-dortmund.de/bitstream/2003/5438/1/148.pdf},
    Year = {2003},
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  • [PDF] T. Bartz-Beielstein, M. Preuss, and A. Reinholz, “Evolutionary algorithms for optimization practitioners,” Universität Dortmund, Germany 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},
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  • [PDF] T. Beielstein, C. Ewald, and S. Markon, “Optimal Elevator Group Control by Evolution Strategies,” in Proceedings genetic and evolutionary computation conf.\textasciitilde(gecco\textasciitilde2003), chicago il, part ii, Berlin, Heidelberg, New York, 2003, pp. 1963-1974.
    [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.{\textasciitilde}(GECCO{\textasciitilde}2003), Chicago IL, Part II},
    Date-Added = {2015-11-29T01:39:36GMT},
    Date-Modified = {2017-03-07 22:50:26 +0000},
    Editor = {u-Paz, E Cant and others},
    File = {{21F3A597-ED20-4E97-B883-32BB2204E0B6.pdf:/Users/bartz/Library/Mobile Documents/com~apple~CloudDocs/Papers3.d/Papers Library/Files/21/21F3A597-ED20-4E97-B883-32BB2204E0B6.pdf:application/pdf}},
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    Pages = {1963--1974},
    Publisher = {Springer},
    Rating = {0},
    Title = {{Optimal Elevator Group Control by Evolution Strategies}},
    Uri = {\url{papers3://publication/uuid/922A739E-46B1-4BB9-B492-23BB70C8CF09}},
    Url = {http://dl.acm.org/citation.cfm?id=1756582.1756684},
    Year = {2003},
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2002

  • [DOI] T. Beielstein and S. Markon, “Threshold Selection, Hypothesis Tests, and DOE Methods,” Technische Universität Dortmund 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 = {2017-03-08 23:12:17 +0000},
    Doi = {http://dx.doi.org/10.17877/DE290R-15303},
    Institution = {Technische Universit\"at Dortmund},
    Keywords = {bartzPublic, free},
    Month = Dezember,
    Publisher = {Universit{\"a}t Dortmund, Germany},
    Rating = {0},
    Title = {{Threshold Selection, Hypothesis Tests, and DOE Methods}},
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    Bdsk-Url-3 = {http://dx.doi.org/10.17877/DE290R-15303}}
  • [DOI] T. Beielstein and S. Markon, “Threshold Selection, Hypothesis Tests, and DOE Methods,” in Proceedings 2002 congress on evolutionary computation (cec’02) within third ieee world congress on computational intelligence (wcci’02), honolulu hi, Piscataway NJ, 2002, pp. 777-782.
    [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}}
  • [DOI] T. Beielstein, J. Dienstuhl, C. Feist, and M. Pompl, “Circuit Design Using Evolutionary Algorithms,” in Proceedings 2002 congress on evolutionary computation (cec’02) within third ieee world congress on computational intelligence (wcci’02), honolulu hi, Piscataway NJ, 2002, pp. 1904-1909.
    [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] T. Beielstein, K. E. Parsopoulos, and M. N. Vrahatis, “Tuning PSO parameters through sensitivity analysis,” 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).},
    Author = {Beielstein, Thomas and Parsopoulos, Konstantinos E and Vrahatis, Michael N},
    Date-Added = {2015-11-29T01:39:36GMT},
    Date-Modified = {2017-03-08 23:36:46 +0000},
    Keywords = {bartzPublic, free},
    Month = jan,
    Publisher = {Universit{\"a}t Dortmund, Germany},
    Rating = {0},
    Title = {{Tuning PSO parameters through sensitivity analysis}},
    Url = {http://hdl.handle.net/2003/5420},
    Year = {2002},
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2001

  • [PDF] T. Beielstein, J. Dienstuhl, C. Feist, and M. Pompl, “Circuit Design Using Evolutionary Algorithms,” Technische Universität Dortmund 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 = {2017-01-14 15:48:37 +0000},
    Institution = {Technische Universit\"at Dortmund},
    Keywords = {bartzPublic, free},
    Month = Dezember,
    Publisher = {Universit{\"a}t Dortmund, Germany},
    Rating = {0},
    Title = {{Circuit Design Using Evolutionary Algorithms}},
    Year = {2001},
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  • [PDF] T. Bartz-Beielstein and others, “Collaborative Research Center 531. Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods (Poster),” , 2001.
    [Bibtex]
    @webpage{Beie01a,
    Author = {Bartz-Beielstein, Thomas and {others}},
    Date-Added = {2015-11-29T01:39:03GMT},
    Date-Modified = {2016-11-15 13:56:29 +0000},
    Keywords = {bartzPublic, free},
    Month = sep,
    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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},
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  • [DOI] S. Markon, D. V. Arnold, T. Bäck, T. Beielstein, and H. Beyer, “Thresholding—A selection operator for noisy ES,” in Proceedings 2001 congress on evolutionary computation (cec’01), seoul, Piscataway NJ, 2001, pp. 465-472.
    [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] H. F. Wedde and T. Beielstein, “Informatik an einer Waldorfschule: Ziele, Erfahrungen, Perspektiven,” Erziehungskunst, vol. 6, pp. 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},
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1995

  • M. Höhfeld, J. Heistermann, C. Kappler, H. Rosé, T. Bäck, T. Beielstein, and B. Naujoks, “Anwendungen evolutionärer Algorithmen,” in Statusseminar des bmbf: bioinformatik, Berlin, Germany, 1995, pp. 281-292.
    [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}}
  • [PDF] T. Bäck, T. Beielstein, B. Naujoks, and J. Heistermann, “Evolutionary Algorithms for the Optimization of Simulation Models using PVM,” in Second european pvm users’ group meeting (europvm’95), Paris, France, 1995, pp. 277-282.
    [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 = {2017-01-14 15:46:35 +0000},
    Editor = {Dongarra, J and Gengler, M and Tourancheau, B and Vigouroux, X},
    File = {{95BB0424-0466-4F35-BF2D-A9BBF110958E.pdf:/Users/bartz/Library/Mobile Documents/com~apple~CloudDocs/Papers3.d/Papers Library/Files/95/95BB0424-0466-4F35-BF2D-A9BBF110958E.pdf:application/pdf}},
    Keywords = {bartzPublic, free},
    Pages = {277--282},
    Publisher = {Herm{\`e}s},
    Rating = {0},
    Title = {{Evolutionary Algorithms for the Optimization of Simulation Models using PVM}},
    Uri = {\url{papers3://publication/uuid/0BBA13E5-45C6-43F3-BA4B-BEBDD8936D07}},
    Year = {1995},
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