Self-Adaptation and Global Convergence : A Counter-Example

dc.contributor.author	Rudolph, Günter	de
dc.date.accessioned	2004-12-07T08:19:59Z
dc.date.available	2004-12-07T08:19:59Z
dc.date.created	1999	de
dc.date.issued	2001-10-16	de
dc.description.abstract	The self-adaptation of the mutation distribution is a distinguishing feature of evolutionary algorithms that optimize over continuous variables. It is widely recognized that self-adaptation accelerates the search for optima and enhances the ability to locate optima accurately, but it is generally unclear whether these optima are global ones or not. Here, it is proven that the probability of convergence to the global optimum is less than one in general even if the objective function is continuous.	en
dc.format.extent	120047 bytes
dc.format.extent	91376 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5368
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15293
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 59	de
dc.subject.ddc	004	de
dc.title	Self-Adaptation and Global Convergence : A Counter-Example	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

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