Optimization of Fuzzy C-Means Clustering by Genetic Algorithms Based on Sizable Chromosome

Abstract

Aiming at the predifined clustering number, strong randomness and easiness to fall into local optimum , a new self-adaptive FCM algorithm based on genetic algorithm is proposed. The number of fuzzy clustering and cluster centers are optimized by sizable-chromosome genetic algorithms (SC-GAs). Cut operator and splice operator are adopted to combination the chromosome to form new individuals. Non-uniform mutation operator is used to enhance the population diversity. The new proposed method can obtain the global optimam compared to standard FCM algorithm. The simulation experimental result s with IRIS demonstrate the feasibility and effectiveness of the new algorithm.