A review of estimation of distribution algorithms in bioinformatics

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.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