Adaptive crossover and mutation in genetic algorithms based on clustering technique

Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By applying the Kmeans algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of px and pm 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 px and pm Categories and Subject Descriptors D.2.2 [Evolutionary prototyping] Keywords Genetic Algorithms and Real World Applications

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Jun Zhang, Henry Shu-Hung Chung, Jinghui Zhong
• Keywords: GECCO 2005, Keywords Genetic Algorithms, Optimized Circuit Component, Power Electronic Circuits