Fuzzy Knowledge Incorporation in Crossover and Mutation

Research on adjusting the probabilities of crossover px and mutation pm 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 chapter 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 logic 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 must meet 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 px and p m .