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An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

Author

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  • Sharon M. McNicholas

    (McMaster University)

  • Paul D. McNicholas

    (McMaster University)

  • Daniel A. Ashlock

    (University of Guelph)

Abstract

An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA facilitates a different search of the fitness landscape, i.e., the likelihood surface, utilizing both crossover and mutation. Furthermore, this EA represents an efficient approach to “hard” model-based clustering and so it can be viewed as a sort of generalization of the k-means algorithm, which is itself equivalent to a restricted Gaussian mixture model. The EA is illustrated on several datasets, and its performance is compared with that of other hard clustering approaches and model-based clustering via the EM algorithm.

Suggested Citation

  • Sharon M. McNicholas & Paul D. McNicholas & Daniel A. Ashlock, 2021. "An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 264-279, July.
    • Handle: RePEc:spr:jclass:v:38:y:2021:i:2:d:10.1007_s00357-020-09371-4
    DOI: 10.1007/s00357-020-09371-4
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    References listed on IDEAS

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