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Progressive Differential Evolution on Clustering Real World Problems

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Artificial Evolution (EA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9554))

Abstract

In this paper, we assess the performances of Differential Evolution on real-world clustering problems. To improve our results, we introduce Progressive Differential Evolution, a small modification of Differential Evolution which aims at optimizing a small number of parameters (eg. one cluster) at the beginning, and incrementally increase the number of optimized parameters.

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Authors and Affiliations

  1. TAO (Inria), LRI, UMR 8623 (CNRS - Univ. Paris-Sud), Bat 660 Claude Shannon Univ. Paris-Sud, 91190, Gif-sur-Yvette, France

    Vincent Berthier

Authors
  1. Vincent Berthier
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Correspondence to Vincent Berthier .

Editor information

Editors and Affiliations

  1. Université Lyon 1, Lyon, France

    Stéphane Bonnevay

  2. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  3. Université de Tours, Tours, France

    Nicolas Monmarché

  4. INRA, Thirverval-Grignon, France

    Evelyne Lutton

  5. Inria Saclay, Université Paris-Sud, Orsay, France

    Marc Schoenauer

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Berthier, V. (2016). Progressive Differential Evolution on Clustering Real World Problems. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2015. Lecture Notes in Computer Science(), vol 9554. Springer, Cham. https://doi.org/10.1007/978-3-319-31471-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-31471-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31470-9

  • Online ISBN: 978-3-319-31471-6

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