Category Archives: Publications

Last chance: Free access to article about Model-based Methods #optimization

If you are interested in “Model-based Methods for Continuous and Discrete Global Optimization“, you can freely access the article until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2
The SPO Toolbox was used for performing the experiments described in this article. The Sequential Parameter Optimization Toolbox 2.0.1 is a major update of the SPOT R package. It provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources. See: https://CRAN.R-project.org/package=SPOT

New Article: Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data #ComputationalIntelligence #MachineLearning

The article “Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data” (Alexis Sardá-Espinosaa, Subanatarajan Subbiah, Thomas Bartz-Beielstein), which will be published in the journal “Engineering Applications of Artificial Intelligence“, can be freely downloaded until May 20, 2017 from
https://authors.elsevier.com/a/1Uojb3OWJ8l3Gq
Anyone who clicks on the link until May 20, 2017, will be taken to the final version of your article on ScienceDirect for free. No sign up or registration is needed – just click and read!

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.

Keywords: Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction

Authors: Alexis Sardá-Espinosa (ABB AG German Research Center, Technische Hochschule Köln),  Subanatarajan Subbiah (ABB AG German Research Center), Thomas Bartz-Beielstein (Technische Hochschule Köln)

Here is the DOI: 10.1016/j.engappai.2017.03.008

 

 

Publish or …

A successful week in the SPOTSeven lab. Three papers (1 x journal, 2 x conference) were accepted for publication:

  • A. Sarda-Espinosa, S. Subbiah, and T. Bartz-Beielstein. Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data. Engineering Applications of Artificial Intelligence, 2017.
  • M. Zaefferer, A. Fischbach, B. Naujoks, and T. Bartz-Beielstein. Simulation-based test functions for optimization algorithms. In GECCO ’17: Proceedings of the 2017 Annual Conference on Genetic and Evolutionary Computation, 2017.
  • J. Heinerman, J. Stork, M. A. R. Coy, J. Hubert, T. Bartz-Beielstein, A. 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.

Interested in #ModelBased Methods for #Optimization? #SPOT2

If you are interested in “Model-based Methods for Continuous and Discrete Global Optimization“, you can freely access the article until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2
The SPO Toolbox was used for performing the experiments described in this article. The Sequential Parameter Optimization Toolbox 2.0.1 is a major update of the SPOT R package. It provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources. See: https://CRAN.R-project.org/package=SPOT

More than 100 Free Publications #ComputationalIntelligence #Optimization on SPOTSeven.org

Today, I updated my publication list. You can find more than 100 PDFs (Computational Intelligence, Optimization, Simulation, Evolutionary Algorithms, Algorithm Tuning, etc.) on http://www.spotseven.de/publications/.

The most recent article Model-based methods for continuous and discrete global optimization
is freely available until April 11, 2017. The following link will direct you to the final version of the article on ScienceDirect (free, without personal or institutional registration) https://authors.elsevier.com/a/1Ub295aecSVmv2

Click and Read! Free Access: Continuous and Discrete Global #Surrogate #Optimization

http://dx.doi.org/10.1016/j.asoc.2017.01.039Just click and read! Everybody can use the following personal article link, which will provide free access to the article “Model-based methods for continuous and discrete global optimization” (Thomas Bartz-Beielstein, Martin Zaefferer), and is valid for 50 days, until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2

Here are some highlights:

  • Up-to-date survey and comprehensive taxonomy of surrogate model based optimization algorithms.
  • Covers continuous and discrete/combinatorial search spaces.
  • Presents six strategies for dealing with discrete data structures.
  • New strategy for model selection and combination in surrogate model-based optimization.
  • Outlook on important challenges (model selection, dimensionality, benchmarks, definiteness) and research directions.

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