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Experimental Analysis of Optimization Algorithms: Tuning and Beyond

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Theory and Principled Methods for the Design of Metaheuristics

Part of the book series: Natural Computing Series ((NCS))

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 computer science, or more concretely, metaheuristic optimization. The experimental 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 adaptability of optimization algorithms. Another branch of experimentation aims at 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 attempt to look into the future to estimate what the hot topics of methodological research will be in the coming years and what changes we may expect for the whole community.

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Notes

  1. 1.

    SPOT can generate 100 randomly chosen design points of the SANN by using the following setting in the CONF file: init.design.size = 100 and init.design.repeats = 1.

  2. 2.

    R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques. CRAN is a network of ftp and web servers around the world that store identical, up-to-date versions of code and documentation for R, see http://cran.r-project.org.

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Acknowledgements

This work was supported by the Bundesministerium für Bildung und Forschung (BMBF) under the grants FIWA (AIF FKZ 17N2309), MCIOP (AIF FKZ 17N0311), and by the Cologne University of Applied Sciences under the research focus grant COSA.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering Science, Institute of Computer Science, Cologne University of Applied Sciences, Cologne, Germany

    Thomas Bartz-Beielstein

  2. Algorithm Engineering, Department of Computer Science, TU Dortmund, Dortmund, Germany

    Mike Preuss

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Correspondence to Thomas Bartz-Beielstein .

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    Yossi Borenstein

  2. University of Birmingham School of Computer Science, Birmingham, United Kingdom

    Alberto Moraglio

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Bartz-Beielstein, T., Preuss, M. (2014). Experimental Analysis of Optimization Algorithms: Tuning and Beyond. In: Borenstein, Y., Moraglio, A. (eds) Theory and Principled Methods for the Design of Metaheuristics. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33206-7_10

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