Analysis of the (1+1) EA for a Dynamically Changing Objective Function

dc.contributor.author	Droste, Stefan	de
dc.date.accessioned	2004-12-07T08:20:53Z
dc.date.available	2004-12-07T08:20:53Z
dc.date.created	2001	de
dc.date.issued	2001-10-30	de
dc.description.abstract	Evolutionary algorithms (EAs) are a class of randomized search heuristics, that are often successfully used for black-box optimization. Nevertheless, there are only few theoretical results about EAs, which are furthermore limited to static objective functions, i. e. functions that do not change over time, despite of the practical relevance of dynamic optimization. Here, the runtime of a simple EA, the (1+1) EA, is theoretically analyzed for a dynamically changing objective function. The main focus lies on determining the degree of change of the fitness funcion, where the expected runtime of the (1+1) EA changes from polynomially to super-polynomially. The proofs presented show methods how to analyze EAs with dynamically changing objective functions.	en
dc.format.extent	147016 bytes
dc.format.extent	328799 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5412
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-7849
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 113	de
dc.subject.ddc	004	de
dc.title	Analysis of the (1+1) EA for a Dynamically Changing Objective Function	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

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