Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5849
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dc.contributor.authorYang, S-
dc.contributor.authorUyar, S-
dc.date.accessioned2011-09-23T11:50:26Z-
dc.date.available2011-09-23T11:50:26Z-
dc.date.issued2006-
dc.identifier.citationThe 21st ACM Symposium on Applied Computing (SAC'06), Dijon, France: 940 - 944, 23-27 Apr 2006en_US
dc.identifier.isbn1-59593-108-2-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5849-
dc.descriptionCopyright @ 2006 ACMen_US
dc.description.abstractIn this paper, a new gene based adaptive mutation scheme is proposed for genetic algorithms (GAs), where the information on gene based fitness statistics and on gene based allele distribution statistics are correlated to explicitly adapt the mutation probability for each gene locus over time. A convergence control mechanism is combined with the proposed mutation scheme to maintain sufficient diversity in the population. Experiments are carried out to compare the proposed mutation scheme to traditional mutation and two advanced adaptive mutation schemes on a set of optimization problems. The experimental results show that the proposed mutation scheme e ciently improves GA's performance.en_US
dc.language.isoenen_US
dc.publisherACMen_US
dc.subjectGenetic algorithmsen_US
dc.subjectGene based adaptive mutationen_US
dc.subjectFitness and allele distribution correlationen_US
dc.titleAdaptive mutation with fitness and allele distribution correlation for genetic algorithmsen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1007/11732242_76-
pubs.organisational-data/Brunel-
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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