A review of estimation of distribution algorithms in bioinformatics

A review of estimation of distribution algorithms in bioinformatics” by Rubén Armañanzas, Iñaki Inza, Roberto Santana, Yvan Saeys, José L. Flores, José A. Lozano, Yves Van de Peer, Rosa Blanco, Vctor Robles, Concha Bielza, and Pedro Larrañaga. BioData Mining, vol. 1, no. 6, 2008, pp. doi:10.1186/1756-0381-1-6.

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.

BibTeX entry:

@article{Armananzas_et_al:2008,
   author = {Rub{\'e}n Arma{\~n}anzas and I{\~n}aki Inza and Roberto
	Santana and Yvan Saeys and Jos{\'e}~L. Flores and Jos{\'e}~A.
	Lozano and Van de Peer, Yves and Rosa Blanco and Vctor Robles and
	Concha Bielza and Pedro Larra{\~n}aga},
   title = {A review of estimation of distribution algorithms in
	bioinformatics},
   journal = {BioData Mining},
   volume = {1},
   number = {6},
   pages = {doi:10.1186/1756-0381-1-6},
   year = {2008},
   url = {http://www.biodatamining.org/content/1/1/6}
}

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