Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution

Authors:
Debarati Kundu;Kaushik Suresh;Sayan Ghosh;Swagatam Das;Ajith Abraham;Youakim Badr
Affiliations:
Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;National Institute of Applied Sciences of Lyon, INSA-Lyon, Villeurbanne, France;National Institute of Applied Sciences of Lyon, INSA-Lyon, Villeurbanne, France
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

This paper applies the Differential Evolution (DE) and Genetic Algorithm (GA) to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performance a hybrid of the GA and DE (GADE) algorithms over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for GADE. The performance of GADE has also been contrasted to that of two most well-known schemes of MO.