title: A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.
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.
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, China.
authors.forename: Shan
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, China.
authors.forename: Liying
authors.identifier: 0000-0002-7752-0392
authors.initials: L
authors.lastname: Kang
authors.affiliation: Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Xing-Ming
authors.initials: XM
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005075:Biological Evolution; D032701:Biomimetics / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008957:Models, Genetic
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