Armañanzas, RubénInza, IñakiSantana, RobertoSaeys, YvanFlores, Jose LuisLozano, José AntonioPeer, Yves van deBlanco Gómez, RosaRobles, VictorBielza, ConchaLarrañaga, Pedro2015-09-302015-09-3020081756-038110.1186/1756-0381-1-6https://academica-e.unavarra.es/handle/2454/18325Evolutionary 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.application/pdfeng© 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.Estimation of distribution algorithmsBioinformaticsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess