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Title: Cooperative co-evolution of GA-based classifiers based on input increments
Authors: Zhu, F
Guan, SU
Keywords: Genetic algorithms;Cooperative co-evolution;Classifiers
Issue Date: 2007
Publisher: Elsevier
Citation: Engineering Applications of Artificial Intelligence. In Press
Abstract: Genetic algorithms (GAs) have been widely used as soft computing techniques in various applications, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, a new cooperative co-evolution algorithm, namely ECCGA, is proposed in the application domain of 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. Some analysis and discussions on ECCGA and possible improvement are also presented.
ISSN: 0952-1976
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Electrical Engineering Research Papers

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