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.

DOI: 10.1186/1756-0381-1-6

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@article{Armaanzas2008ARO, title={A review of estimation of distribution algorithms in bioinformatics}, author={Rub{\'e}n Arma{\~n}anzas and I{\~n}aki Inza and Roberto Santana and Yvan Saeys and Jose Luis Flores and Jos{\'e} Antonio Lozano and Yves Van de Peer and Rosa Blanco and V{\'i}ctor Robles and Concha Bielza and Pedro Larra{\~n}aga}, journal={BioData Mining}, year={2008}, volume={1}, pages={6 - 6} }