- author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
- title = "A comparison of genetic programming variants for data classification",
- booktitle = "Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)",
- year = "1999",
- editor = "Eric Postma and Marc Gyssens",
- pages = "253--254",
- address = "Kasteel Vaeshartelt, Maastricht, Holland",
- month = "3-4 " # nov,
- organisation = "BNVKI, Dutch and the Belgian AI Association",
- keywords = "genetic algorithms, genetic programming, data mining, classification",
-
URL = "
http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
-
URL = "http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz",
- size = "2 pages",
- 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",
-
notes = "resubmission of
\cite{EEH99b}
https://documentserver.uhasselt.be/handle/1942/4193
July 2024 broken http://www.cs.unimaas.nl/~bnvki/",
