Co-evolutionary rule-chaining genetic programming

Wing Ho SHUM; Kwong Sak LEUNG; Man Leung WONG

doi:10.1007/11508069_71

Co-evolutionary rule-chaining genetic programming

Wing Ho SHUM, Kwong Sak LEUNG, Man Leung WONG

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning, IDEAL 2005, 6th International Conference, Brisbane, Australia, July 6-8, 2005 : proceedings
Publisher	Springer
Pages	546-554
Number of pages	9
DOIs	https://doi.org/10.1007/11508069_71
Publication status	Published - 1 Jan 2005

Bibliographical note

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

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10.1007/11508069_71

Cite this

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title = "Co-evolutionary rule-chaining genetic programming",

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.",

author = "SHUM, {Wing Ho} and LEUNG, {Kwong Sak} and WONG, {Man Leung}",

note = "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",

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Co-evolutionary rule-chaining genetic programming. / SHUM, Wing Ho; LEUNG, Kwong Sak; WONG, Man Leung.
Intelligent Data Engineering and Automated Learning, IDEAL 2005, 6th International Conference, Brisbane, Australia, July 6-8, 2005 : proceedings. Springer, 2005. p. 546-554.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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Co-evolutionary rule-chaining genetic programming

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