The use of fuzzy connectives to design real-coded genetic algorithms

• Autores: Francisco Herrera Triguero, Manuel Lozano Márquez, José Luis Verdegay Galdeano
• Localización: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology, ISSN-e 1134-5632, Vol. 1, Nº. 3, 1994, págs. 239-251
• Idioma: inglés
• Títulos paralelos:
  • El uso de conectivos difusos en el diseño de algoritmos genéticos con codificación real
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• 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.