Constraint handling in genetic algorithms using a gradient-based repair method

Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes to use the gradient information derived from the constraint set to systematically repair infeasible solutions. The proposed repair procedure is embe...

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Bibliographic Details
Published inComputers & operations research Vol. 33; no. 8; pp. 2263 - 2281
Main Authors CHOOTINAN, Piya, CHEN, Anthony
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Science 01.08.2006
Pergamon Press Inc
Subjects
Applied sciences
DNA repair
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Genetics
Operational research and scientific management
Operational research. Management science
Operations research
Optimization algorithms
Constraint handling
Hybrid method
Genetic algorithm
Benchmarks
Stochastic method
Search algorithm
Constrained optimization
Genetic algorithms
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Summary:Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes to use the gradient information derived from the constraint set to systematically repair infeasible solutions. The proposed repair procedure is embedded into a simple GA as a special operator. Experiments using 11 benchmark problems are presented and compared with the best known solutions reported in the literature. Our results are competitive, if not better, compared to the results reported using the homomorphous mapping method, the stochastic ranking method, and the self-adaptive fitness formulation method. [PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2005.02.002