A Review of Estimation of Distribution Algorithms in Bioinformatics
Ikusi/ Ireki
Data
2008-09-11Egilea
Armañanzas Arnedillo, Rubén
Saeys, Yvan
Flores Barroso, Jose Luis
Lozano Alonso, José Antonio
Van de Peer, Yves
Blanco, Rosa
Robles Forcada, Víctor
Bielza, Concha
Larrañaga Múgica, Pedro
BioData Mining 1 : (2008) // Article ID 6
Laburpena
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.