Evolutionary algorithm using feasibility-based grouping for numerical constrained optimization problems

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Different strategies for defining the relationship between feasible and infeasible individuals in evolutionary algorithms can provide with very different results when solving numerical constrained optimization problems. This paper proposes a novel EA to balance the relationship between feasible and infeasible individuals to solve numerical constrained optimization problems. According to the feasibility of the individuals, the population is divided into two groups, feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. Parents for reproduction are selected from the two groups by a novel parent selection method. The proposed method is tested using (mu,lambda) evolution strategies with 13 benchmark problems. The results show that the proposed method improves the searching performance for most of the tested problems. (c) 2005 Elsevier Inc. All rights reserved.
Publisher
ELSEVIER SCIENCE INC
Issue Date
2006-04
Language
English
Article Type
Article
Citation

APPLIED MATHEMATICS AND COMPUTATION, v.175, pp.1298 - 1319

ISSN
0096-3003
URI
http://hdl.handle.net/10203/12970
Appears in Collection
EE-Journal Papers(저널논문)
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