@inproceedings{conf/ideal/ShumLW05,
title = {Co-evolutionary Rule-Chaining Genetic Programming},
address = {Brisbane, Australia},
author = {Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong},
booktitle = {Intelligent Data Engineering and Automated Learning -
IDEAL 2005, 6th International Conference, Proceedings},
editor = {Marcus Gallagher and James M. Hogan and Fr{\'e}d{\'e}ric Maire},
month = {July 6-8},
pages = {546--554},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {3578},
year = {2005},
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.},
bibsource = {DBLP,
http://dblp.uni-trier.de/db/conf/ideal/ideal2005.html#ShumLW05}, size = {9 pages}, bibdate = {2005-06-23}, isbn = {3-540-26972-X}, 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}, doi = {doi:10.1007/11508069_71},
keywords = {Agents Complex Systems algorithms, and genetic programming, }
}
::