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dc.contributor.authorKorejo, Imtiaz
dc.contributor.authorYang, Shengxiang
dc.contributor.authorBrohi, Kamran
dc.contributor.authorKhuhro, Z.U.A.
dc.date.accessioned2020-01-07T13:56:05Z
dc.date.available2020-01-07T13:56:05Z
dc.date.issued2013-04
dc.identifier.citationKorejo, I., Yang, S., Brohi, K., and Khuhro, Z.U.A. (2013) Multi-population methods with adaptive mutation for multi-modal optimization problems. International Journal on Soft Computing, Artificial Intelligence and Application, 2(2), pp.1-19.en
dc.identifier.issn2319-4081
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/18995
dc.descriptionopen access journalen
dc.description.abstractThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms.en
dc.language.isoen_USen
dc.publisherAirCC Publishingen
dc.subjectMulti-population approachesen
dc.subjectadaptive mutation operatoren
dc.subjectmulti-modal function optimizationen
dc.subjectgenetic algorithmsen
dc.titleMulti-population methods with adaptive mutation for multi-modal optimization problemsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.5121/ijscai.2013.2201
dc.peerreviewedYesen
dc.funderNo external funderen
dc.cclicenceN/Aen
dc.date.acceptance2013-02
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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