To combine steady-state genetic algorithm and ensemble learning for data clustering


Research output: Journal PublicationsJournal Article (refereed)peer-review

35 Citations (Scopus)


This paper proposes a data clustering algorithm that combines the steady-state genetic algorithm and the ensemble learning method, termed as genetic-guided clustering algorithm with ensemble learning operator (GCEL). GCEL adopts the steady-state genetic algorithm to perform the search task, but replaces its traditional recombination operator with an ensemble learning operator. Therefore, GCEL can avoid the problems of clustering invalidity and context insensitivity of the traditional recombination operator of genetic algorithms. In addition, GCEL generates its initial population of candidate clustering solutions by using the random subspaces method. Therefore, less fitness evaluations are required to converge. The proposed GCEL is tested on one synthetic and several real data sets. Experimental results demonstrate that GCEL is able to achieve a comparative or better clustering solution with less fitness evaluations when compared with several other existing genetic-guided clustering algorithms. © 2008 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)1416-1423
JournalPattern Recognition Letters
Issue number9
Publication statusPublished - 1 Jul 2008
Externally publishedYes

Bibliographical note

This paper was supported by the Project No. 7002073, City University of Hong Kong. The authors would like to thank the constructive comments from the reviewers.


  • Clustering analysis
  • Ensemble learning
  • Genetic-guided clustering algorithms


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