title: A review of estimation of distribution algorithms in bioinformatics.
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.
authors.affiliation: Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia - San Sebastián, Spain. ruben@si.ehu.es
authors.forename: Rubén
authors.initials: R
authors.lastname: Armañanzas
authors.forename: Iñaki
authors.initials: I
authors.lastname: Inza
authors.forename: Roberto
authors.initials: R
authors.lastname: Santana
authors.forename: Yvan
authors.initials: Y
authors.lastname: Saeys
authors.forename: Jose Luis
authors.initials: JL
authors.lastname: Flores
authors.forename: Jose Antonio
authors.initials: JA
authors.lastname: Lozano
authors.forename: Yves
authors.initials: Y
authors.lastname: Van de Peer
authors.forename: Rosa
authors.initials: R
authors.lastname: Blanco
authors.forename: Víctor
authors.initials: V
authors.lastname: Robles
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga