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BioData Mining

, 1:6

First Online: 11 September 2008Received: 18 January 2008Accepted: 11 September 2008


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.

Electronic supplementary materialThe online version of this article doi:10.1186-1756-0381-1-6 contains supplementary material, which is available to authorized users.

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Autor: Rubén Armañanzas - Iñaki Inza - Roberto Santana - Yvan Saeys - Jose Luis Flores - Jose Antonio Lozano - Yves Van de 

Fuente: https://link.springer.com/

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