A comparison of genetic programming variants for data
classification
J. Eggermont, A. Eiben, and J. van Hemert. Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99), page 253--254. Kasteel Vaeshartelt, Maastricht, Holland, (3-4 November 1999)
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). 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
%0 Conference Paper
%1 EEH99bnaic
%A Eggermont, J.
%A Eiben, A. E.
%A van Hemert, J. I.
%B Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99)
%C Kasteel Vaeshartelt, Maastricht, Holland
%D 1999
%E Postma, Eric
%E Gyssens, Marc
%K algorithms, classification data genetic mining, programming,
%P 253--254
%T A comparison of genetic programming variants for data
classification
%U http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz
%X 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). 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
@inproceedings{EEH99bnaic,
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). 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},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Kasteel Vaeshartelt, Maastricht, Holland},
author = {Eggermont, J. and Eiben, A. E. and {van Hemert}, J. I.},
biburl = {https://www.bibsonomy.org/bibtex/22de10321a4bd02a68233c6e81230274d/brazovayeye},
booktitle = {Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99)},
editor = {Postma, Eric and Gyssens, Marc},
interhash = {8a55488ca35a07a806c5813c25e29abc},
intrahash = {2de10321a4bd02a68233c6e81230274d},
keywords = {algorithms, classification data genetic mining, programming,},
month = {3-4 November},
notes = {resubmission of \cite{EEH99b}
http://www.cs.unimaas.nl/~bnvki/},
organisation = {BNVKI, Dutch and the Belgian AI Association},
pages = {253--254},
size = {2 pages},
timestamp = {2008-06-19T17:39:07.000+0200},
title = {A comparison of genetic programming variants for data
classification},
url = {http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz},
year = 1999
}