Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms

dc.contributor.author	Horoba, Christian	de
dc.contributor.author	Neumann, Frank	de
dc.date.accessioned	2009-05-12T16:02:21Z
dc.date.available	2009-05-12T16:02:21Z
dc.date.issued	2008-12	de
dc.description.abstract	Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the e-dominance approach and point out how and when such mechanisms provably help to obtain good additive approximations of the Pareto-optimal set.	en
dc.identifier.uri	http://hdl.handle.net/2003/26164
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-9019
dc.language.iso	en	de
dc.relation.ispartofseries	Reihe CI; 257-08	de
dc.subject.ddc	004	de
dc.title	Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms	en
dc.type	Text	de
dc.type.publicationtype	report	de
dcterms.accessRights	open access

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