Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary multi-objective clustering for overlapping clusters detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
International Journal of Hybrid Intelligent Systems
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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.