A comparison of genetic programming variants for data classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7989
- @InProceedings{EEH99bnaic,
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author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
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title = "A comparison of genetic programming variants for data
classification",
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booktitle = "Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99)",
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year = "1999",
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editor = "Eric Postma and Marc Gyssens",
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pages = "253--254",
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address = "Kasteel Vaeshartelt, Maastricht, Holland",
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month = "3-4 " # nov,
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organisation = "BNVKI, Dutch and the Belgian AI Association",
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keywords = "genetic algorithms, genetic programming, data mining,
classification",
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URL = "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
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URL = "http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz",
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size = "2 pages",
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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",
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notes = "resubmission of
\cite{EEH99b}
https://documentserver.uhasselt.be/handle/1942/4193
July 2024 broken http://www.cs.unimaas.nl/~bnvki/",
- }
Genetic Programming entries for
Jeroen Eggermont
Gusz Eiben
Jano I van Hemert
Citations