Journal Title
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Publisher
Springer, Berlin, Heidelberg
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Authors: Selmar K Smit Agoston E Eiben
Publish Date: 2010
Volume: , Issue: , Pages: 287-310
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
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