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

  • Debarati Kundu
  • Kaushik Suresh
  • Sayan Ghosh
  • Swagatam Das
  • Ajith Abraham
  • Youakim Badr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

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.

Keywords

Differential Evolution Multiobjective Optimisation Differential Evolution Algorithm Adjust Rand Index Automatic Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Debarati Kundu
    • 1
  • Kaushik Suresh
    • 1
  • Sayan Ghosh
    • 1
  • Swagatam Das
    • 1
  • Ajith Abraham
    • 2
  • Youakim Badr
    • 2
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.National Institute of Applied Sciences of Lyon, INSA-LyonVilleurbanneFrance

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