Optimization of Fuzzy C-Means Clustering by Genetic Algorithms Based on Sizable Chromosome
Jie‐Sheng Wang, Xianwen Gao · 2009
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 results with IRIS demonstrate the feasibility and effectiveness of the new algorithm.