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dc.contributor.authorWang, Hongfengen
dc.contributor.authorWang, Dingweien
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-05-17T10:19:06Z
dc.date.available2013-05-17T10:19:06Z
dc.date.issued2009
dc.identifier.citationWang, H., Wang, D. and Yang, S. (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Computing, 13(8-9), July 2009, pp. 763-780.en
dc.identifier.issn1432-7643
dc.identifier.urihttp://hdl.handle.net/2086/8589
dc.description.abstractDynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.en
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.subjectGenetic algorithm (GA)en
dc.subjectMemetic algorithmen
dc.subjectLocal searchen
dc.subjectCrossover-basedhill climbingen
dc.subjectMutation-based hill climbingen
dc.subjectDual mappingen
dc.subjectTriggeredrandom immigrantsen
dc.subjectDynamic optimization problems (DOP)en
dc.titleA memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1007/s00500-008-0347-3
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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