Multi-Objective Evolutionary Clustering using Variable-Length Real Jumping Genes Genetic Algorithm
Kazi Shah Nawaz Ripon, Chi-Ho Tsang, Sam Kwong, Man-Ki Ip · 2006
In this paper, we present a novel multi-objective evolutionary clustering approach using Variable-length Real Jumping Genes Genetic Algorithms (VRJGGA). The proposed algorithm that extends Jumping Genes Genetic Algorithm (JGGA) [1] evolves near-optimal clustering solutions using multiple clustering criteria, without apriori knowledge of the actual number of clusters. Experimental results based on several artificial and realworld data show that VRJGGA can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance.