Handling Constraints in Genetic Algorithms

Cezary Janikow; Zbigniew Michalewicz

Handling Constraints in Genetic Algorithms

Cezary Janikow, Zbigniew Michalewicz

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Abstract

The major difficulty in applicability of genetic algorithms to various optimization problems is the lack of general methodology for handling constraints. This paper discusses a new such methodology and presents results from the experimental system GENOCOP (for GEnetic algorithm for Numerical Optimization for COnstrainted Problems). The system not only handles any objective function with any set of linear constraints, but also effectively reduces the search space. The results indicate that this approach is superior to traditional methods when applied to the nonlinear transportation problem.

Original language	American English
Journal	Proceedings of the Fourth International Conference on Genetic Algorithms
State	Published - Jul 13 1991

Disciplines

Computer Sciences

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Cite this

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title = "Handling Constraints in Genetic Algorithms",

abstract = " The major difficulty in applicability of genetic algorithms to various optimization problems is the lack of general methodology for handling constraints. This paper discusses a new such methodology and presents results from the experimental system GENOCOP (for GEnetic algorithm for Numerical Optimization for COnstrainted Problems). The system not only handles any objective function with any set of linear constraints, but also effectively reduces the search space. The results indicate that this approach is superior to traditional methods when applied to the nonlinear transportation problem.",

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AB - The major difficulty in applicability of genetic algorithms to various optimization problems is the lack of general methodology for handling constraints. This paper discusses a new such methodology and presents results from the experimental system GENOCOP (for GEnetic algorithm for Numerical Optimization for COnstrainted Problems). The system not only handles any objective function with any set of linear constraints, but also effectively reduces the search space. The results indicate that this approach is superior to traditional methods when applied to the nonlinear transportation problem.

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JF - Proceedings of the Fourth International Conference on Genetic Algorithms

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