Co-evolutionary Rule-Chaining Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/ideal/ShumLW05,
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title = "Co-evolutionary Rule-Chaining Genetic Programming",
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author = "Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong",
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year = "2005",
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pages = "546--554",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "3578",
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booktitle = "Intelligent Data Engineering and Automated Learning -
IDEAL 2005, 6th International Conference, Proceedings",
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editor = "Marcus Gallagher and James M. Hogan and
Fr{\'e}d{\'e}ric Maire",
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address = "Brisbane, Australia",
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month = jul # " 6-8",
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bibdate = "2005-06-23",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ideal/ideal2005.html#ShumLW05",
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keywords = "genetic algorithms, genetic programming, Agents and
Complex Systems",
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ISBN = "3-540-26972-X",
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DOI = "doi:10.1007/11508069_71",
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size = "9 pages",
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abstract = "Genetic Programming (GP) paradigm called
Co-evolutionary Rule-Chaining Genetic Programming
(CRGP) has been proposed to learn the relationships
among attributes represented by a set of classification
rules for multi-class problems. It employs backward
chaining inference to carry out classification based on
the acquired acyclic rule set. Its main advantages are:
1) it can handle more than one class at a time; 2) it
avoids cyclic result; 3) unlike Bayesian Network (BN),
the CRGP can handle input attributes with continuous
values directly; and 4) with the flexibility of GP,
CRGP can learn complex relationship. We have
demonstrated its better performance on one synthetic
and one real-life medical data sets.",
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notes = "(1) Department of Computer Science and Engineering,
The Chinese University of Hong Kong, Shatin, Hong Kong
(2) Department of Information Systems, Lingnan
University, Tuen Mun, Hong Kong",
- }
Genetic Programming entries for
Wing-Ho Shum
Kwong-Sak Leung
Man Leung Wong
Citations