Adaptive crossover, mutation and selection using fuzzy system for genetic algorithms

Abstract

Genetic algorithms use a tournament selection or a roulette selection to choice better population. But these selections couldnt use heuristic information for specific problem. Fuzzy selection system by heuristic rule base help to find optimal solution efficiently. And adaptive crossover and mutation probabilistic rate is faster than using fixed value. In this paper, we want fuzzy selection system for genetic algorithms and adaptive crossover and mutation rate fuzzy system. © International Symposium on Artificial Life and Robotics (ISAROB). 2008.