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Adaptive Genetic Algorithms Based on Fuzzy Techniques (1996)
| Venue: | In Proc. of IPMU'96 |
| Citations: | 14 - 0 self |
BibTeX
@INPROCEEDINGS{Herrera96adaptivegenetic,
author = {F. Herrera and M. Lozano},
title = {Adaptive Genetic Algorithms Based on Fuzzy Techniques},
booktitle = {In Proc. of IPMU'96},
year = {1996},
pages = {775--780}
}
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Abstract
The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algorithms are built using fuzzy logic techniques. In this paper, we summarize two types of such approaches. The first one concerns dynamic crossover operators based on parameterized fuzzy connectives and the second one deals with adaptive real-coded genetic algorithms based on the use of fuzzy logic controllers. 1 INTRODUCTION GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities that might eventually evolve into something even better. The loss of critical alleles due to selection pressure, the selection noise, the schemata disruption due to crossover operator, and poor parameter setting may make this exploitation/exploration r...
Keyphrases
adaptive genetic algorithm fuzzy technique exploitation exploration fuzzy logic controller fuzzy logic technique introduction ga behaviour schema disruption adaptive real-coded genetic algorithm genetic algorithm behaviour concern dynamic crossover operator selection noise suitable exploitation exploration relationship parameterized fuzzy connective exploration relationship poor parameter setting premature convergence problem critical allele
