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dc.contributor.authorSantana Hermida, Roberto
dc.contributor.authorBielza, Concha
dc.contributor.authorLarrañaga, Pedro
dc.contributor.authorLozano Alonso, José Antonio ORCID
dc.contributor.authorEchegoyen, Carlos
dc.contributor.authorMendiburu Alberro, Alexander
dc.contributor.authorArmañanzas Arnedillo, Rubén
dc.contributor.authorShakya, Siddartha
dc.date.accessioned2014-03-31T18:25:31Z
dc.date.available2014-03-31T18:25:31Z
dc.date.issued2010-07
dc.identifier.citationJournal of Statistical Software 35(7) : 1-30 (2010)es
dc.identifier.issn1548-7660
dc.identifier.urihttp://hdl.handle.net/10810/11887
dc.description.abstractThis paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.es
dc.description.sponsorshipPartially supported by the Saiotek and Research Groups 2007-2012 (IT-242-07) programs (Basque Government), TIN2008-06815-C02-01, TIN- 2008-06815-C02-02, TIN2007-62626 and Consolider Ingenio 2010 - CSD2007-00018 projects (Spanish Ministry of Science and Innovation), the CajalBlueBrain project, and the COMBIOMED network in computational biomedicine (Carlos III Health Institute).es
dc.language.isoenges
dc.publisherJournal of Statistical Software, UCLA Dept. Statisticses
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectestimation of distribution algorithmses
dc.subjectprobabilistic modelses
dc.subjectstatistical learning;es
dc.subjectoptimizationes
dc.subjectMATLABes
dc.subjectevolutionary algorithmses
dc.subjectKikuchi approximationses
dc.subjectmodel;es
dc.subjectclassifieres
dc.subjectnetworkses
dc.titleMateda-2.0: Estimation of Distribution Algorithms in MATLABes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderThis work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3)es
dc.relation.publisherversionhttp://www.jstatsoft.org/v35/i07es
dc.departamentoesArquitectura y Tecnología de Computadoreses_ES
dc.departamentoeuKonputagailuen Arkitektura eta Teknologiaes_ES
dc.subject.categoriaSOFTWARE
dc.subject.categoriaSTATISTICS AND PROBABILITY


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