A review of estimation of distribution algorithms in bioinformatics
| dc.contributor.author | Armañanzas, Rubén | |
| dc.contributor.author | Inza, Iñaki | |
| dc.contributor.author | Santana, Roberto | |
| dc.contributor.author | Saeys, Yvan | |
| dc.contributor.author | Flores, Jose Luis | |
| dc.contributor.author | Lozano, José Antonio | |
| dc.contributor.author | Peer, Yves van de | |
| dc.contributor.author | Blanco Gómez, Rosa | |
| dc.contributor.author | Robles, Victor | |
| dc.contributor.author | Bielza, Concha | |
| dc.contributor.author | Larrañaga, Pedro | |
| dc.contributor.department | Estadística e Investigación Operativa | es_ES |
| dc.contributor.department | Estatistika eta Ikerketa Operatiboa | eu |
| dc.date.accessioned | 2015-09-30T07:35:25Z | |
| dc.date.available | 2015-09-30T07:35:25Z | |
| dc.date.issued | 2008 | |
| 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. | en |
| 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). R. Armañanzas is supported by Basque Government grant AE-BFI-05/430. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1186/1756-0381-1-6 | |
| dc.identifier.issn | 1756-0381 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/18325 | |
| dc.language.iso | eng | en |
| dc.publisher | BioMed Central | en |
| dc.relation.ispartof | BioData Mining 2008, 1:6 | en |
| dc.relation.publisherversion | https://dx.doi.org/10.1186/1756-0381-1-6 | |
| dc.rights | © 2008 Armañanzas et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/2.0/ | |
| dc.subject | Estimation of distribution algorithms | en |
| dc.subject | Bioinformatics | en |
| dc.title | A review of estimation of distribution algorithms in bioinformatics | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 01ee4e6b-5d15-405b-945b-912d3d55a7e0 | |
| relation.isAuthorOfPublication.latestForDiscovery | 01ee4e6b-5d15-405b-945b-912d3d55a7e0 |