The Use of Fuzzy Connectivesto DesignReal - Coded Genetic Algorithms 1

Abstract

Genetic algorithms are adaptive methods that use principles inspired by natural population genetics to evolve solutions to 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; the search becomes trapped in a local optimum before the global optimum is found. Fuzzy logic techniques may be used for solving this problem. This paper presents one of them: the design of crossover operators for real-coded genetic algorithms using fuzzy connectives and its extension based on the use of parameterized fuzzy connectives as tools for tackling the premature convergence problem.

Cite this paper

@inproceedings{Herrera1995TheUO, title={The Use of Fuzzy Connectivesto DesignReal - Coded Genetic Algorithms 1}, author={F. Herrera and Miguel - Angel Sanchis - Lozano and J . L . VerdegayDept}, year={1995} }