Co-evolutionary Rule-Chaining Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/ideal/ShumLW05,

• title = "Co-evolutionary Rule-Chaining Genetic Programming",
• author = "Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong",
• year = "2005",
• pages = "546--554",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3578",
• 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",
• address = "Brisbane, Australia",
• month = jul # " 6-8",
• bibdate = "2005-06-23",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ideal/ideal2005.html#ShumLW05",
• keywords = "genetic algorithms, genetic programming, Agents and Complex Systems",
• ISBN = "3-540-26972-X",
• DOI = "doi:10.1007/11508069_71",
• size = "9 pages",
• 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.",
• 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",

}

Genetic Programming entries for Wing-Ho Shum Kwong-Sak Leung Man Leung Wong

Citations