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A comparison of genetic programming variants for data classification

TitleA comparison of genetic programming variants for data classification
Publication TypeConference Paper
Year of Publication1999
AuthorsEggermont, J, Eiben, AE, van Hemert, JI
Conference NameSpringer Lecture Notes on Computer Science
PublisherSpringer-Verlag, Berlin
EditorHand, DJ, Kok, JN, Berthold, MR
ISBN Number3-540-66332-0
Keywordsclassification; data mining; genetic programming
Abstract

In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body. As a third aspect we look at generational and steady-state models in combination of both features.

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