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dc.contributor.authorTinos, Renatoen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2017-03-07T11:07:47Z
dc.date.available2017-03-07T11:07:47Z
dc.date.issued2007-09-01
dc.identifier.citationTinos, R. and Yang, S. (2007) A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines, 8 (3), pp. 255-286en
dc.identifier.urihttp://hdl.handle.net/2086/13440
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
dc.language.isoen_USen
dc.publisherSpringeren
dc.subjectGenetic algorithmsen
dc.subjectSelf-organized criticalityen
dc.subjectDynamic optimization problemsen
dc.subjectRandom immigrantsen
dc.titleA self-organizing random immigrants genetic algorithm for dynamic optimization problemsen
dc.typeArticleen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderFAPESPen
dc.projectid04/04289-6en
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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