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dc.contributor.authorUnanue, I.
dc.contributor.authorMerino, M. 
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2019-09-08T20:24:37Z
dc.date.available2019-09-08T20:24:37Z
dc.date.issued2019-07-01
dc.identifier.isbn978-1-4503-6748-6
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1007
dc.description.abstractEstimation of Distribution Algorithms have been successfully used for solving many combinatorial optimization problems. One type of problems in which Estimation of Distribution Algorithms have presented strong competitive results are permutation-based combinatorial optimization problems. In this case, the algorithms use probabilistic models specifically designed for codifying probability distributions over permutation spaces. One class of these probability models is distance-based exponential models, and one example of this class is the Mallows model. In spite of the practical success, the theoretical analysis of Estimation of Distribution Algorithms for permutation-based combinatorial optimization problems has not been extensively developed. With this motivation, this paper presents a first mathematical analysis of the convergence behavior of Estimation of Distribution Algorithms based on the Mallows model by using an infinite population to associate a dynamical system to the algorithm. Several scenarios, with different fitness functions and initial probability distributions of increasing complexity, are analyzed obtaining unexpected results in some cases.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.titleA mathematical analysis of edas with distance-based exponential modelsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1145/3319619.3321969
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?id=3321969en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleGECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companionen_US


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