The use of fuzzy connectives to design real-coded genetic algorithms
ISSN: 1134-5632
Año de publicación: 1994
Volumen: 1
Número: 3
Páginas: 239-251
Tipo: Artículo
Otras publicaciones en: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology
Resumen
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 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.