Runtime analysis for maximizing population diversity in single-objective optimization

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dc.contributor.authorGao, W.
dc.contributor.authorNeumann, F.
dc.contributor.conference2014 Annual Conference on Genetic and Evolutionary Computation (GECCO'14) (12 Jul 2014 - 16 Jul 2014 : Vancouver, Canada)
dc.contributor.editorIgel, C.
dc.date.issued2014
dc.description.abstractRecently Ulrich and Thiele [14] have introduced evolutionary algorithms for the mixed multi-objective problem of maximizing fitness as well as diversity in the decision space. Such an approach allows to generate a diverse set of solutions which are all of good quality. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for maximizing the diversity in a population that contains several solutions of high quality. We study how evolutionary algorithms maximize the diversity of a population where each individual has to have fitness beyond a given threshold value. We present a first runtime analysis in this area and study the classical problems called OneMax and LeadingOnes. Our results give first rigorous insights on how evolutionary algorithms can be used to produce a maximal diverse set of solutions in which all solutions have quality above a certain threshold value.
dc.description.statementofresponsibilityWanru Gao, Frank Neumann
dc.identifier.citationProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014 / Igel, C. (ed./s), pp.777-784
dc.identifier.doi10.1145/2576768.2598251
dc.identifier.isbn9781450326629
dc.identifier.orcidGao, W. [0000-0002-7805-0919]
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/109118
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.granthttp://purl.org/au-research/grants/arc/DP130104395
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140103400
dc.rightsCopyright 2014 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
dc.source.urihttp://dl.acm.org/citation.cfm?id=2576768
dc.subjectRuntime analysis, single objective optimization, diversity optimization
dc.titleRuntime analysis for maximizing population diversity in single-objective optimization
dc.typeConference paper
pubs.publication-statusPublished

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