dc.contributor.author | Armañanzas Arnedillo, Rubén | |
dc.contributor.author | Inza Cano, Iñaki ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Santana Hermida, Roberto ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Saeys, Yvan | |
dc.contributor.author | Flores Barroso, Jose Luis | |
dc.contributor.author | Lozano Alonso, José Antonio | |
dc.contributor.author | Van de Peer, Yves | |
dc.contributor.author | Blanco, Rosa | |
dc.contributor.author | Robles Forcada, Víctor | |
dc.contributor.author | Bielza, Concha | |
dc.contributor.author | Larrañaga Múgica, Pedro | |
dc.date.accessioned | 2019-04-15T18:44:41Z | |
dc.date.available | 2019-04-15T18:44:41Z | |
dc.date.issued | 2008-09-11 | |
dc.identifier.citation | BioData Mining 1 : (2008) // Article ID 6 | es_ES |
dc.identifier.issn | 1756-0381 | |
dc.identifier.uri | http://hdl.handle.net/10810/32491 | |
dc.description.abstract | Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. | es_ES |
dc.description.sponsorship | This work has been partially supported by the 2007-2012 Etortek, Saiotek and Research Group (IT-242-07) programs (Basque Government), TIN2005-03824 and Consolider Ingenio 2010-CSD2007-00018 projects (Spanish Ministry of Education and Science) and the COMBIOMED network in computational biomedicine (Carlos III Health Institute). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Biomed Central | es_ES |
dc.relation | info:eu-repo/grantAgreement/MEC/2010-CSD2007-00018 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | feature-selection | es_ES |
dc.subject | molecular classification | es_ES |
dc.subject | gene interactions | es_ES |
dc.subject | feature ranking | es_ES |
dc.subject | prediction | es_ES |
dc.subject | optimization | es_ES |
dc.subject | networks | es_ES |
dc.subject | cancer | es_ES |
dc.title | A Review of Estimation of Distribution Algorithms in Bioinformatics | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://biodatamining.biomedcentral.com/articles/10.1186/1756-0381-1-6 | es_ES |
dc.identifier.doi | 10.1186/1756-0381-1-6 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |