Title

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

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

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