Evolution strategies with q-Gaussian mutation for dynamic optimization problems.

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dc.contributor.authorTinos, Renatoen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-05-24T09:44:00Z
dc.date.available2013-05-24T09:44:00Z
dc.date.issued2010
dc.description.abstractEvolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on four experiments generated from the simulation of evolutionary robots.en
dc.identifier.citationTinos, R. and Yang, S. (2010) Evolution strategies with q-Gaussian mutation for dynamic optimization problems. In: 2010 Eleventh Brazilian Symposium on Neural Networks (SBRN), Sao Paulo, October 2010. New York: IEEE, pp.en
dc.identifier.doihttps://doi.org/10.1109/SBRN.2010.46
dc.identifier.isbn978-0-7695-4210-2
dc.identifier.urihttp://hdl.handle.net/2086/8678
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherIEEEen
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectEvolution strategiesen
dc.subjectDynamic environmentsen
dc.subjectEvolutionary algorithmen
dc.subjectq-Gaussian mutationen
dc.subjectRoboticsen
dc.titleEvolution strategies with q-Gaussian mutation for dynamic optimization problems.en
dc.typeConferenceen

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