Fuzzy Tools to Improve Genetic Algorithms (1994)
| Venue: | Proc. of the Second European Congress on Intelligent Techniques and Soft Computing |
| Citations: | 13 - 7 self |
BibTeX
@INPROCEEDINGS{Herrera94fuzzytools,
author = {F. Herrera and F. Herrera and E. Herrera-viedma and E. Herrera-viedma and M. Lozano and M. Lozano and J. L. Verdegay and J. L. Verdegay},
title = {Fuzzy Tools to Improve Genetic Algorithms},
booktitle = {Proc. of the Second European Congress on Intelligent Techniques and Soft Computing},
year = {1994},
pages = {1532--1539}
}
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Abstract
We propose two fuzzy tools to improve the genetic algorithms behaviour. The use of fuzzy connectives to design crossover operators for real coded genetic algorithms (RCGA), and the use of fuzzy logic based systems for the dynamic control paprameters of RCGA based on diversity measures between chromosomes and alleles. 1. Introduction Genetic algorithms (GA) are search algorithms that use operations found in natural genetics to guide the trek through a search space. GA are theoretically and empirically proven to provide robust search in complex spaces, giving a valid approach to problems requiring efficient and effective search [Gol89]. A GA generally emploies three operators: selection, crossover, mutation. The selection operator is formulated after the darwinian principle of survival of the fittest, whereas the crossover and mutation operators have been inspired by the mechanisms of gene mutation and chromosome recombination found in biological genetics. Their computational role is to...
