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dc.contributor.authorCeberio, J.
dc.contributor.authorMendiburu, A.
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2018-08-31T12:18:01Z
dc.date.available2018-08-31T12:18:01Z
dc.date.issued2018-08-30
dc.identifier.isbn978-145035764-7
dc.identifier.urihttp://hdl.handle.net/20.500.11824/850
dc.description.abstractEstimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained type problems. The great majority of works in the literature implement external repairing or penalty schemes, or use ad-hoc sampling methods in order to avoid unfeasible solutions. In this work, we present a new way to develop EDAs for this type of problems by implementing distance-based exponential probability models defined exclusively on the set of feasible solutions. In order to illustrate this procedure, we take the 2-partition balanced Graph Partitioning Problem as a case of study, and design efficient learning and sampling methods in order to use these distance-based probability models in EDAs.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.titleDistance-based exponential probability models on constrained combinatorial optimization problemsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1145/3205651.3205659
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?doid=3205651.3205659en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companionen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España