Adaptive probabilities of crossover and mutation in genetic algorithms based on clustering technique

Research on adjusting the probabilities of crossover p_x and mutation p_m in genetic algorithms (GA's) is one of the most significant and promising areas of investigation in evolutionary computation, since p_x and p_m greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of having fixed p_x and p_m, this paper presents the use of fuzzy logic to adaptively tune p_x and P_m for optimization of power electronic circuits throughout the process. By applying the A-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of p _x and p_m, are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed p_x and p_m.