(BioMed Central, 2008) Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, José Antonio; Peer, Yves van de; Blanco Gómez, Rosa; Robles, Victor; Bielza, Concha; Larrañaga, Pedro; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
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.
