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An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization (1998)
| Venue: | Proceedings of the Third Annual Genetic Programming Conference |
| Citations: | 9 - 2 self |
BibTeX
@INPROCEEDINGS{Rasheed98anadaptive,
author = {Khaled Rasheed},
title = {An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization},
booktitle = {Proceedings of the Third Annual Genetic Programming Conference},
year = {1998},
pages = {584--590},
publisher = {Morgan Kaufmann Publishers}
}
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Abstract
In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach. 1 Introduction Genetic Algorithms (GAs) (Goldberg 1989) are search algorithms that mimic the behavior of natural selection. GAs attempt to find the best solution to some problem (e.g., the maximum of a function) by generating a collection ("population ") of potential solutions ("individuals") to the problem. Through mutation and recombination (crossover) operations, better solutions are hopefully generated out of the current set of potential solutions. This process continues until an acceptably good solution is found. GAs have many advantages over other search techniques, including the ability to deal with qualitativ...
Keyphrases
adaptive penalty approach constrained genetic-algorithm optimization potential solution many advantage introduction genetic algorithm genetic algorithm optimization problem new adaptive penalty approach natural selection search technique good solution several engineering design small penalty coefficient gas attempt current set empirical result
