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dc.contributor.authorTinos, Renatoen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2017-03-07T11:14:25Z
dc.date.available2017-03-07T11:14:25Z
dc.date.issued2007-07-01
dc.identifier.citationYang, S. and Tinos, R. (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing, 4 (3), pp. 243-254en
dc.identifier.urihttp://hdl.handle.net/2086/13441
dc.description.abstractDynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.en
dc.language.isoen_USen
dc.publisherSpringeren
dc.subjectGenetic algorithmsen
dc.subjectRandom immigrantsen
dc.subjectElitism-based immigrantsen
dc.subjectdualismen
dc.subjectDynamic optimization problemsen
dc.titleA hybrid immigrants scheme for genetic algorithms in dynamic environmentsen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.1007/s11633-007-0243-9
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/E060722/01en
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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