Co-evolutionary rule-chaining genetic programming
Document Type
Conference paper
Source Publication
Intelligent Data Engineering and Automated Learning, IDEAL 2005, 6th International Conference, Brisbane, Australia, July 6-8, 2005 : proceedings
Publication Date
1-1-2005
First Page
546
Last Page
554
Publisher
Springer
Abstract
A novel 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.
DOI
10.1007/11508069_71
Print ISSN
03029743
Publisher Statement
Copyright © Springer-Verlag Berlin Heidelberg 2005. Access to external full text or publisher's version may require subscription.
Additional Information
Paper presented at the 6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005), 6-8 July 2005, Brisbane, Australia.
ISBN of the source publication: 9783540269724
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Shum, W.-H., Leung, K.-s., & Wong, M.-L. (2005). Co-evolutionary rule-chaining genetic programming. In M. Gallagher, J. P. Hogan, & f. Maire (Eds.), Intelligent Data Engineering and Automated Learning, IDEAL 2005, 6th International Conference, Brisbane, Australia, July 6-8, 2005: Proceedings (pp. 546-554). Springer. doi: 10.1007/11508069_71