Messy genetic algorithm for clustering

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations


A new algorithm is proposed for clustering, drawing on the ideas underlying messy genetic algorithms. Individuals in each generation are clusters whose fitness depends on the mean distance between samples in the same cluster. The algorithm consists of four phases: initialization, cluster-formation, operator-application, and final cluster-selection. Four operators are used: merge, split, erode, and gobble. The algorithm is fairly general, and the function to be optimized can be modified to obtain good clusters that satisfy different requirements.

Original languageEnglish (US)
Number of pages6
StatePublished - 1993
EventProceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93 - St.Louis, MO, USA
Duration: Nov 14 1993Nov 17 1993


OtherProceedings of the 1993 Artificial Neural Networks in Engineering, ANNIE'93
CitySt.Louis, MO, USA

ASJC Scopus subject areas

  • Software


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