Asymptotical Convergence Rates of Simple Evolutionary Algorithms under Factorizing Mutation Distributions

dc.contributor.authorRudolph, Güntherde
dc.date.accessioned2004-12-07T08:19:12Z
dc.date.available2004-12-07T08:19:12Z
dc.date.created1997de
dc.date.issued1998-11-06de
dc.description.abstractThe standard choice for mutating an individual of an evolutionary algorithm with continuous variables is the normal distribution. It is shown that there is a broad class of alternative mutation distributions offering local convergence rates being asymptotical equal to the convergence rates achieved with normally distributed mutations. Such mutation distributions must be factorizing and the absolute fourth moments must be finite. Under these conditions an asymptotical theory of the convergence rates of simple evolutionary algorithms can be established for the entire class of distributions.en
dc.format.extent198409 bytes
dc.format.extent460590 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5320
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15158
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 8de
dc.subject.ddc004de
dc.titleAsymptotical Convergence Rates of Simple Evolutionary Algorithms under Factorizing Mutation Distributionsen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

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