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

Piya Chootinan, Anthony Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

175 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)2263-2281
Number of pages19
JournalComputers and Operations Research
Volume33
Issue number8
DOIs
Publication statusPublished - 1 Aug 2006
Externally publishedYes

Keywords

  • Constrained optimization
  • Constraint handling
  • Genetic algorithms
  • Hybrid method

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Applied Mathematics
  • Modelling and Simulation
  • Transportation

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