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Using Entropy for Parameter Analysis of Evolutionary Algorithms

  • Selmar K. SmitEmail author
  • Agoston E. EibenEmail author
Chapter

Abstract

Evolutionary algorithms (EA) form a rich class of stochastic search methods that share the basic principles of incrementally improving the quality of a set of candidate solutions by means of variation and selection (Eiben and Smith 2003, De Jong 2006). Such variation and selection operators often require parameters to be specified. Finding a good set of parameter values is a nontrivial problem in itself. Furthermore, some EA parameters are more relevant than others in the sense that choosing different values for them affects EA performance more than for the other parameters. In this chapter we explain the notion of entropy and discuss how entropy can disclose important information about EA parameters, in particular, about their relevance. We describe an algorithm that is able to estimate the entropy of EA parameters and we present a case study, based on extensive experimentation, to demonstrate the usefulness of this approach and some interesting insights that are gained.

Keywords

Evolutionary Algorithm Evolutionary Computation Candidate Solution Mutation Operator Shannon Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgement

All experimental data and results are due to Volker Nannen.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands

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