Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5804
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dc.contributor.authorYang, S-
dc.date.accessioned2011-09-16T09:53:52Z-
dc.date.available2011-09-16T09:53:52Z-
dc.date.issued2008-
dc.identifier.citationEvolutionary Computation, 16(3): 385 - 416, Sep 2008en_US
dc.identifier.issn1063-6560-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5804-
dc.descriptionCopyright @ 2008 by the Massachusetts Institute of Technologyen_US
dc.description.abstractIn recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.en_US
dc.description.sponsorshipThis work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/01.en_US
dc.language.isoenen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.subjectGenetic algorithmsen_US
dc.subjectDynamic optimization problemsen_US
dc.subjectMemoryen_US
dc.subjectRandom immigrantsen_US
dc.subjectMemory-based immigrantsen_US
dc.subjectElitism-based immigrantsen_US
dc.titleGenetic algorithms with memory- and elitism-based immigrants in dynamic environmentsen_US
dc.typeResearch Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1162/evco.2008.16.3.385-
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-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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