An improved fuzzy genetic algorithm with fuzzy adjusted crossover and mutation probabilities
Zhidong Qi, Chunming Peng · 2010
In order to overcome the shortcomings of the standard multi-objective genetic algorithm, an improved fuzzy genetic algorithm and its structure are proposed based on the fuzzy reasoning theory. A fuzzy controller is used to adjust the genetic algorithms' crossover probabilities and mutation probabilities. At the same time, the best fuzzy rules of the fuzzy controller will be found during the optimizing process. The results of simulation on two typical mathematical functions show that this fuzzy genetic algorithm can improve both the convergent speed and the quality of the solution.