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dc.contributor.authorTinós, R-
dc.contributor.authorYang, S-
dc.identifier.citationInternational Journal of Hybrid Intelligent Systems, 8(3): 155 - 168, August 2011en_US
dc.descriptionCopyright @ IOS Press. All Rights Reserved.en_US
dc.description.abstractEvolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator.en_US
dc.publisherIOS Pressen_US
dc.subjectq-Gaussian mutationen_US
dc.subjectEvolution strategiesen_US
dc.subjectDynamic optimization problemsen_US
dc.titleSelf-adaptation of mutation distribution in evolution strategies for dynamic optimization problemsen_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-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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