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

Kundu Debarati (1, 2) , Sayan Ghosh (1, 2) , Suresh Kaushik (1, 2) , Das Swagatam (1, 2) , Ajith Abraham (1) , Youakim Badr (1)

1 LIESP - Laboratoire d'Informatique pour l'Entreprise et les Systèmes de Production
2 Department of Electronics and Telecommunication Engineering

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.

Domaines: Intelligence artificielle [cs.AI]

Kundu Debarati, Sayan Ghosh, Suresh Kaushik, Das Swagatam, Ajith Abraham, et al.. Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution. Conference on Hybrid Artificial Intelligent Systems, Jun 2009, Salamanca, Spain. pp.177-186. ⟨hal-00389059⟩