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Dynamic and heuristic fuzzy connectives‐based crossover operators for controlling the diversity and convergence of real‐coded genetic algorithms
Author(s) -
Herrera F.,
Lozano M.,
Verdegay J. L.
Publication year - 1996
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199612)11:12<1013::aid-int1>3.0.co;2-q
Subject(s) - crossover , premature convergence , heuristic , mathematical optimization , genetic algorithm , fuzzy logic , population , operator (biology) , computer science , algorithm , convergence (economics) , genetic operator , mutation , mathematics , artificial intelligence , meta optimization , biochemistry , chemistry , demography , repressor , sociology , transcription factor , economics , gene , economic growth
Genetic algorithms are adaptive methods which may be used as approximation heuristic for search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover, and mutation. A great problem in the use of genetic algorithms is the premature convergence, a premature stagnation of the search caused by the lack of diversity in the population and a disproportionate relationship between exploitation and exploration. The crossover operator is considered one of the most determinant elements for solving this problem. In this article we present two types of crossover operators based on fuzzy connectives for real‐coded genetic algorithms. The first type is designed to keep a suitable sequence between the exploration and the exploitation along the genetic algorithm's run, the dynamic fuzzy connectives‐based crossover operators, the second, for generating offspring near to the best parents in order to offer diversity or convergence in a profitable way, the heuristic fuzzy connectives‐based crossover operators. We combine both crossover operators for designing dynamic heuristic fuzzy connectives‐based crossover operators that show a robust behavior. © 1996 John Wiley & Sons, Inc.