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Adapting the Fitness Function in GP for Data Mining

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Genetic Programming (EuroGP 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1598))

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Abstract

In this paper we describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in EAs for constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is wellsuited for data mining tasks where the fitness of a candidate solution is composed by ‘local scores’ on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.

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References

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Authors and Affiliations

  1. Leiden University, P.O. Box 9512, 2300, RA, Leiden, The Netherlands

    J. Eggermont, A. E. Eiben & J. I. van Hemert

Authors
  1. J. Eggermont
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  2. A. E. Eiben
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  3. J. I. van Hemert
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Egdbaston, Birmingham, B15 2TT, UK

    Riccardo Poli

  2. Department of Physical Resource Theory, Chalmers University of Technology, S-412 96, Göteborg, Sweden

    Peter Nordin

  3. Centrum voorWiskunde en Informatic, Kruislaan 413, 1098, SJ Amsterdam, The Netherlands

    William B. Langdon

  4. Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, UK

    Terence C. Fogarty

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© 1999 Springer-Verlag Berlin Heidelberg

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Eggermont, J., Eiben, A.E., van Hemert, J.I. (1999). Adapting the Fitness Function in GP for Data Mining. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds) Genetic Programming. EuroGP 1999. Lecture Notes in Computer Science, vol 1598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48885-5_16

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  • DOI: https://doi.org/10.1007/3-540-48885-5_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65899-3

  • Online ISBN: 978-3-540-48885-9

  • eBook Packages: Springer Book Archive

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