A review of estimation of distribution algorithms in bioinformatics.
BioData Min
; 1(1): 6, 2008 Sep 11.
Article
en En
| MEDLINE
| ID: mdl-18822112
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.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
BioData Min
Año:
2008
Tipo del documento:
Article
País de afiliación:
España