Co-evolutionary rule-chaining genetic programming

Authors:
Wing-Ho Shum;Kwong-sak Leung;Man-Leung Wong
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Information Systems, Lingnan University, Hong Kong
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

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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.