Genetic clustering algorithms

Yu-Chiun Chiou, Lawrence W. Lan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

This study employs genetic algorithms to solve clustering problems. Three models, SICM, STCM, CSPM, are developed according to different coding/decoding techniques. The effectiveness and efficiency of these models under varying problem sizes are analyzed in comparison to a conventional statistics clustering method (the agglomerative hierarchical clustering method). The results for small scale problems (10-50 objects) indicate that CSPM is the most effective but least efficient method, STCM is second most effective and efficient, SICM is least effective because of its long chromosome. The results for medium-to-large scale problems (50-200 objects) indicate that CSPM is still the most effective method. Furthermore, we have applied CSPM to solve an exemplified p-Median problem. The good results demonstrate that CSPM is usefully applicable.

Original languageEnglish
Pages (from-to)413-427
Number of pages15
JournalEuropean Journal of Operational Research
Volume135
Issue number2
DOIs
StatePublished - 1 Dec 2001

Keywords

  • Clustering
  • Genetic algorithms
  • p-Median problem

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