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.
Armañanzas
Rubén
R
Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia - San Sebastián, Spain. ruben@si.ehu.es
Inza
Iñaki
I
Santana
Roberto
R
Saeys
Yvan
Y
Flores
Jose Luis
JL
Lozano
Jose Antonio
JA
Van de Peer
Yves
Y
Blanco
Rosa
R
Robles
Víctor
V
Bielza
Concha
C
Larrañaga
Pedro
P