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dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-05-16T09:02:14Z
dc.date.available2013-05-16T09:02:14Z
dc.date.issued2011
dc.identifier.citationTinos, R and Yang, S. (2011) Use of the q-Gaussian mutation in evolutionary algorithms. Soft Computing, 15(8), August 2011, pp. 1523-1549.en
dc.identifier.issn1432-7643
dc.identifier.urihttp://hdl.handle.net/2086/8507
dc.description.abstractThis paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.en
dc.language.isoenen
dc.publisherSpringer-Verlag.en
dc.subjectEvolutionary algorithmsen
dc.subjectq-Gaussian distributionen
dc.subjectSelf-adaptationen
dc.subjectEvolutionary programmingen
dc.subjectMutation distributionen
dc.titleUse of the q-Gaussian mutation in evolutionary algorithms.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1007/s00500-010-0686-8
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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