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Comparing genetic programming variants for data classification
23 August 2007 - 1:54pm — Jano.van.Hemert
| Title | Comparing genetic programming variants for data classification |
| Publication Type | Conference Paper |
| Year of Publication | 1999 |
| Authors | Eggermont, J, Eiben, AE, van Hemert, JI |
| Conference Name | Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99) |
| Publisher | BNVKI, Dutch and the Belgian AI Association |
| Editor | Postma, E, Gyssens, M |
| Keywords | classification; 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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