Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-87656-4_15
Title: Enhanced cooperative co-evolution genetic algorithm for rule-based pattern classification
Authors: Zhu, F. 
Guan, S.-U.
Keywords: Classifiers
Cooperative co-evolution
Genetic algorithms
Issue Date: 2008
Citation: Zhu, F., Guan, S.-U. (2008). Enhanced cooperative co-evolution genetic algorithm for rule-based pattern classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5271 LNAI : 113-123. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-87656-4_15
Abstract: Genetic algorithms (GAs) have been widely used as soft computing techniques in various application domains, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, an enhanced cooperative co-evolution genetic algorithm (ECCGA) is proposed for rule-based pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA. © 2008 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/129634
ISBN: 3540876553
ISSN: 03029743
DOI: 10.1007/978-3-540-87656-4_15
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