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dc.contributor.authorYang, S-
dc.identifier.citation8th Annual Conference on Genetic and Evolutionary Computation (GECCO'06)_, Seattle, Washington, USA: 1435 - 1436, 8 - 12 Jul 2007en_US
dc.descriptionCopyright @ 2006 Yangen_US
dc.description.abstractThis paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance probability, which is learned adaptively during the searching progress and hence is adapted to the dynamic environment. Using a series of dynamic test problems, the proposed dominance scheme is compared to two other dominance schemes for diploidy genetic algorithms. The experimental results validate the efficiency of the proposed dominance learning scheme.en_US
dc.subjectDiploid genetic algorithmsen_US
dc.subjectDominance change schemeen_US
dc.subjectDominance learningen_US
dc.subjectDynamic optimization problemsen_US
dc.titleDominance learning in diploid genetic algorithms for dynamic optimization problemsen_US
dc.typeConference Paperen_US
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
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Computer Science
Dept of Computer Science Research Papers

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