Approximating the Pareto set

dc.contributor.authorLaumanns, Marcode
dc.contributor.authorRudolph, Güntherde
dc.contributor.authorSchwefel, Hans-Paulde
dc.date.accessioned2004-12-07T08:20:09Z
dc.date.available2004-12-07T08:20:09Z
dc.date.created1999de
dc.date.issued2001-10-16de
dc.description.abstractThis paper adresses the problem of diversity in multiobjective evolutionary algorithms and its implications for the quality of the approximated set of efficient solutions (Pareto set). Current approaches for maintaining diversity are classified and related to the overall fitness assignment strategy. The resulting groups of complex selection operators are presented and tested on different objective functions exhibiting different levels of difficulty. For the assessment of the algorithmic performance a quality measure based on the notion of dominance is applied that reflects gain of information produced by the algorithm. This allows an online and time-dependent evaluation in order to characterize the dynamic behavior of an algorithm.en
dc.description.abstractThis paper adresses the problem of diversity in multiobjective evolutionary algorithms and its implications for the quality of the approximated set of efficient solutions (Pareto set). Current approaches for maintaining diversity are classified and related to the overall fitness assignment strategy. The resulting groups of complex selection operators are presented and tested on different objective functions exhibiting different levels of difficulty. For the assessment of the algorithmic performance a quality measure based on the notion of dominance is applied that reflects gain of information produced by the algorithm. This allows an online and time-dependent evaluation in order to characterize the dynamic behavior of an algorithm.en
dc.format.extent243161 bytes
dc.format.extent268874 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5377
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15334
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 72de
dc.subject.ddc004de
dc.titleApproximating the Pareto seten
dc.title.alternativeconcepts, diversity issues, and performance assessmenten
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

Files

Original bundle
Now showing 1 - 2 of 2
Loading...
Thumbnail Image
Name:
ci72.pdf
Size:
262.57 KB
Format:
Adobe Portable Document Format
Description:
DNB
No Thumbnail Available
Name:
ci72.ps
Size:
237.46 KB
Format:
Postscript Files