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dc.contributor.authorTinos, R-
dc.contributor.authorYang, S-
dc.identifier.citationGenetic Programming and Evolvable Machines, 8(3): 255 - 286, Sep 2007en_US
dc.descriptionThis is the post-print version of the article. The official published version can be obtained from the link below - Copyright @ 2007 Springeren_US
dc.description.abstractIn this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.en_US
dc.description.sponsorshipThis work was supported by FAPESP (Proc. 04/04289-6).en_US
dc.subjectGenetic algorithmsen_US
dc.subjectSelf-organized criticalityen_US
dc.subjectDynamic optimization problemsen_US
dc.subjectRandom immigrantsen_US
dc.titleA self-organizing random immigrants genetic algorithm 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-
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Computer Science
Dept of Computer Science Research Papers

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