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European Conference on Genetic Programming

EuroGP 2000: Genetic Programming pp 76–90Cite as

Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff – Introductory Investigations

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1802))

Abstract

The decomposition of regression error into bias and variance terms provides insight into the generalization capability of modeling methods. The paper offers an introduction to bias/variance decomposition of mean squared error, as well as a presentation of experimental results of the application of genetic programming. Finally ensemble methods such as bagging and boosting are discussed that can reduce the generalization error in genetic programming.

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Author information

Authors and Affiliations

  1. Danish Hydraulic Institute, Agern Alle 5, Hørsholm, Denmark

    Maarten Keijzer & Vladan Babovic

Authors
  1. Maarten Keijzer
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  2. Vladan Babovic
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Editor information

Editors and Affiliations

  1. Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Riccardo Poli

  2. Department of Computer Science, Memorial University of Newfoundland, Canada

    Wolfgang Banzhaf

  3. Departments of Mathematical, Biological Sciences and Computing and Electronic Systems, University of Essex, UK

    William B. Langdon

  4. School of Computer Science, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Julian Miller

  5. Dept. of Physical Rescource Theory, Chalmers University of Technology, 412 96, Göteborg, Sweden

    Peter Nordin

  6. School of Computing, Information Systems and Mathematics, South Bank University, SE1 0AA, London, UK

    Terence C. Fogarty

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© 2000 Springer-Verlag Berlin Heidelberg

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Keijzer, M., Babovic, V. (2000). Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff – Introductory Investigations. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_6

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  • DOI: https://doi.org/10.1007/978-3-540-46239-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67339-2

  • Online ISBN: 978-3-540-46239-2

  • eBook Packages: Springer Book Archive

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