Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
Jun Zhang, Henry Shu-Hung Chung, Alan Wai-lun Lo · IEEE Transactions on Evolutionary Computation · 2007
Research into adjusting the probabilities of crossover and mutation pmin genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. pxand pmgreatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of pxand pm, this paper presents the use of fuzzy logic to adaptively adjust the values of pxand pmin GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of pxand pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of pxand pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions