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Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems

TitleAdaptive Genetic Programming Applied to New and Existing Simple Regression Problems
Publication TypeConference Paper
Year of Publication2001
AuthorsEggermont, J, van Hemert, JI
Conference NameSpringer Lecture Notes on Computer Science
PublisherSpringer-Verlag, Berlin
EditorMiller, J, Tomassini, M, Lanzi, PL, Ryan, C, Tettamanzi, AGB, Langdon, WB
ISBN Number9-783540-418993
Keywordsdata mining
Abstract

In this paper we continue our study on adaptive genetic pro-gramming. We use Stepwise Adaptation of Weights to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression prob-lems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.

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