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Comparing genetic programming variants for data classification

TitleComparing genetic programming variants for data classification
Publication TypeConference Paper
Year of Publication1999
AuthorsEggermont, J, Eiben, AE, van Hemert, JI
Conference NameProceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
PublisherBNVKI, Dutch and the Belgian AI Association
EditorPostma, E, Gyssens, M
Keywordsclassification; data mining; genetic programming
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

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