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On the roles of redundancy and neutrality in evolutionary optimization

dc.contributor.authorCorreia, Marisol B.
dc.contributor.authorFonseca, C. M.
dc.date.accessioned2012-03-23T19:19:55Z
dc.date.available2012-03-23T19:19:55Z
dc.date.issued2007
dc.date.updated2012-03-21T10:22:16Z
dc.description.abstractAn experimental study was performed to explore whether it is neutrality itself or simply the larger neighborhoods associ- ated with neutral representations that influence the results achieved by evolutionary algorithms on NK fitness landscape problems. Markov chains were used to model the behaviour of a stochastic hill-climber on NK fitness landscapes, using two different types of representation: a neutral network rep- resentation which exhibits neutrality and a redundant rep- resentation without neutrality which implements the same neighborhood induced by the corresponding neutral repre- sentation.por
dc.identifier.citationCorreia, Marisol B.; Fonseca, Carlos M. On the roles of redundancy and neutrality in evolutionary optimization, Trabalho apresentado em the 9th annual conference, In Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07, London, England, 2007.por
dc.identifier.issn9781595936974
dc.identifier.otherAUT: MCO00732;
dc.identifier.urihttp://hdl.handle.net/10400.1/948
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherACM Presspor
dc.titleOn the roles of redundancy and neutrality in evolutionary optimizationpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceLondon, Englandpor
oaire.citation.titleProceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07por
person.familyNameCorreia
person.givenNameMarisol B.
person.identifier.ciencia-idFE15-819A-4535
person.identifier.orcid0000-0002-1788-6114
person.identifier.scopus-author-id55333058000
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublicationee01864e-dec4-4285-a4b7-4ccb53d9afaa
relation.isAuthorOfPublication.latestForDiscoveryee01864e-dec4-4285-a4b7-4ccb53d9afaa

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