Associative memory scheme for genetic algorithms in dynamic environments

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dc.cclicenceN/Aen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2017-03-14T16:44:50Z
dc.date.available2017-03-14T16:44:50Z
dc.date.issued2006
dc.description.abstractIn recent years dynamic optimization problems have attracted a growing interest from the community of genetic algorithms with several approaches developed to address these problems, of which the memory scheme is a major one. In this paper an associative memory scheme is proposed for genetic algorithms to enhance their performance in dynamic environments. In this memory scheme, the environmental information is also stored and associated with current best individual of the population in the memory. When the environment changes the stored environmental information that is associated with the best re-evaluated memory solution is extracted to create new individuals into the population. Based on a series of systematically constructed dynamic test environments, experiments are carried out to validate the proposed associative memory scheme. The environmental results show the efficiency of the associative memory scheme for genetic algorithms in dynamic environments.en
dc.funderN/Aen
dc.identifier.citationYang, S. (2006) Associative memory scheme for genetic algorithms in dynamic environments. EvoWorkshops 2006: Applications of Evolutionary Computing, Lecture Notes in Computer Science, 3907, pp. 788-799en
dc.identifier.urihttp://hdl.handle.net/2086/13589
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectidN/Aen
dc.publisherSpringeren
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectAssociative memory schemeen
dc.subjectGenetic algorithmen
dc.subjectDynamic environmentsen
dc.titleAssociative memory scheme for genetic algorithms in dynamic environmentsen
dc.typeConferenceen

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