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Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems

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Genetic Programming (EuroGP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2038))

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Abstract

In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three gp variants.

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Author information

Authors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Leiden University, Leiden

    Jeroen Eggermont & Jano I. van Hemert

Authors
  1. Jeroen Eggermont
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  2. Jano I. van Hemert
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham

    Julian Miller

  2. Computer Science Institute, University of Lausanne, Lausanne

    Marco Tomassini

  3. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy

    Pier Luca Lanzi

  4. Computer Science and Information Systems, University of Limerick, Limerick

    Conor Ryan

  5. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  6. Department of Computer Science, University College London, London

    William B. Langdon

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© 2001 Springer-Verlag Berlin Heidelberg

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Eggermont, J., van Hemert, J.I. (2001). Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_3

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  • DOI: https://doi.org/10.1007/3-540-45355-5_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41899-3

  • Online ISBN: 978-3-540-45355-0

  • eBook Packages: Springer Book Archive

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