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Seeding Genetic Programming Populations

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Genetic Programming (EuroGP 2000)

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

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Abstract

We show genetic programming (GP) populations can evolve under the influence of a Pareto multi-objective fitness and program size selection scheme, from “perfect” programs which match the training material to general solutions. The technique is demonstrated with programmatic image compression, two machine learning benchmark problems (Pima Diabetes and Wisconsin Breast Cancer) and an insurance customer profiling task (Benelearn99 data mining).

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Author information

Authors and Affiliations

  1. Centrum voor Wiskunde en Informatica, Kruislaan 413, NL-1098 SJ, Amsterdam, The Netherlands

    W. B. Langdon

  2. Chalmers University of Technology, S-41296, Gøteborg, Sweden

    J. P. Nordin

Authors
  1. W. B. Langdon
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  2. J. P. Nordin
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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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Langdon, W.B., Nordin, J.P. (2000). Seeding Genetic Programming Populations. 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_23

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  • DOI: https://doi.org/10.1007/978-3-540-46239-2_23

  • 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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