Adaptive crossover and mutation in genetic algorithms based on clustering technique

Authors:
Jun Zhang;Henry S. H. Chung;Jinghui Zhong
Affiliations:
SUN Yat-sen University, Guangzhou, China;City University of Hong Kong;SUN Yat-sen University, Guangzhou, China
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Abstract

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 K-means 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 p.