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Paper ID: 2720

Comparing genetic programming variants for data classification
Eggermont,J. Eiben,A.E. van Hemert,J.I. ,

Appeared in: Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99),
Page Numbers:253--254
Publisher: BNVKI, Dutch and the Belgian AI Association
Year: 1999
ISBN/ISSN:
Contributing Organisation(s):
Field of Science: e-Science

URL:

Abstract: This article is a combined summary of two papers written by the authors. Binary data classification problems (with exactly two disjoint classes) form an important application area of machine learning techniques, in particular genetic programming (GP). In this study we compare a number of different variants of GP applied to such problems whereby we investigate the effect of two significant changes in a fixed GP setup in combination with two different evolutionary models

Keywords: data mining, genetic programming, classification,


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