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An Efficient Constraint Handling Method for Genetic Algorithms (1998)
| Venue: | Computer Methods in Applied Mechanics and Engineering |
| Citations: | 246 - 19 self |
BibTeX
@INPROCEEDINGS{Deb98anefficient,
author = {Kalyanmoy Deb},
title = {An Efficient Constraint Handling Method for Genetic Algorithms},
booktitle = {Computer Methods in Applied Mechanics and Engineering},
year = {1998},
pages = {311--338}
}
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Abstract
Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using genetic algorithms (GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function approach is generic and applicable to any type of constraint (linear or nonlinear), their performance is not always satisfactory. Thus, researchers have developed sophisticated penalty functions specific to the problem at hand and the search algorithm used for optimization. However, the most difficult aspect of the penalty function approach is to find appropriate penalty parameters needed to guide the search towards the constrained optimum. In this paper, GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are explo...
Keyphrases
genetic algorithm efficient constraint handling method constrained optimization problem penalty function approach population-based approach sophisticated penalty function tournament selection operator popular approach optimization problem penalty function method constrained optimum pair-wise comparison many real-world search classical optimization method difficult aspect search algorithm equality constraint appropriate penalty parameter
