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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationHybrid Artificial Intelligence Systems - 4th International Conference, HAIS 2009, Proceedings
Pages177-186
Number of pages10
DOIs
StatePublished - 2009
Event4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009 - Salamanca, Spain
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5572 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009
Country/TerritorySpain
CitySalamanca
Period6/10/096/12/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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