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Paper ID: 2719

Adapting the Fitness Function in GP for Data Mining
Eggermont,J. Eiben,A.E. van Hemert,J.I. ,

Appeared in: Springer Lecture Notes on Computer Science,
Page Numbers:195--204
Publisher: Springer-Verlag, Berlin
Year: 1999
ISBN/ISSN: 3-540-65899-8
Contributing Organisation(s):
Field of Science: e-Science

URL:

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 well suited 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.

Keywords: data mining, genetic programming,


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