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BioMed Research International
Volume 2014, Article ID 362738, 8 pages
http://dx.doi.org/10.1155/2014/362738
Review Article

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

1Department of Mathematics, Shanghai University, Shanghai 200444, China
2Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

Received 3 December 2013; Revised 29 January 2014; Accepted 29 January 2014; Published 6 March 2014

Academic Editor: Jean X. Gao

Copyright © 2014 Shan Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.