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 work 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 K-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.