Improved genetic algorithms with fuzzy logic controlled crossover and mutation

Genetic operators such as crossover and mutation have significant impact on the performance of a genetic algorithm. In this paper, two fuzzy controllers have been designed to adaptively adjust the crossover probability and mutation rate during the optimization process based on some heuristics. The implementation of fuzzy crossover and mutation controllers have been described in detail. As an example, environmental constrained economic dispatch problem has been used to demonstrate the performances of the proposed fuzzy controlled genetic algorithms. The results are very encouraging.