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Population Clustering in Genetic Programming

  • Conference paper
Genetic Programming (EuroGP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3905))

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Abstract

This paper proposes an approach to reducing the cost of fitness evaluation whilst improving the effectiveness in Genetic Programming (GP). In our approach, the whole population is first clustered by a heuristic called fitness-case-equivalence. Then a cluster representative is selected for each cluster. The fitness value of the representative is calculated on all training cases. The fitness is then directly assigned to other members in the same cluster. Subsequently, a clustering tournament selection method replaces the standard tournament selection method. A series of experiments were conducted to solve a symbolic regression problem, a binary classification problem, and a multi-class classification problem. The experiment results show that the new GP system significantly outperforms the standard GP system on these problems.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand

    Huayang Xie, Mengjie Zhang & Peter Andreae

Authors
  1. Huayang Xie
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  2. Mengjie Zhang
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  3. Peter Andreae
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Editor information

Editors and Affiliations

  1. Université Louis Pasteur, LSIIT, FDBT, Pôle API,, F-67400, Illkirch, France

    Pierre Collet

  2. Institut des Systèmes d’Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC),, Switzerland

    Marco Tomassini

  3. Lehrstuhl für Informatik II, Universität Würzburg, Am Hubland,, 97074, Würzburg, Germany

    Marc Ebner

  4. GE Global Research, Niskayuna,, NY, USA

    Steven Gustafson

  5. Aston University, Birmingham, UK

    Anikó Ekárt

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© 2006 Springer-Verlag Berlin Heidelberg

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Xie, H., Zhang, M., Andreae, P. (2006). Population Clustering in Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_17

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  • DOI: https://doi.org/10.1007/11729976_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33143-8

  • Online ISBN: 978-3-540-33144-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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