Review

A review of estimation of distribution algorithms in bioinformatics

Rubén Armañanzas¹ , Iñaki Inza¹ , Roberto Santana¹ , Yvan Saeys^2,3 , Jose Luis Flores¹ , Jose Antonio Lozano¹ , Yves Van de Peer^2,3 , Rosa Blanco⁴ , Víctor Robles⁵ , Concha Bielza⁶ and Pedro Larrañaga⁶

¹  Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia – San Sebastián, Spain

²  Department of Plant Systems Biology, Ghent University, Ghent, Belgium

³  Department of Molecular Genetics, Ghent University, Ghent, Belgium

⁴  Department of Statistics and Operations Research, Public University of Navarre, Pamplona, Spain

⁵  Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain

⁶  Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

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BioData Mining 2008, 1:6doi:10.1186/1756-0381-1-6

Published:	11 September 2008

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.