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Title: Cooperative co-evolution of GA-based classifiers based on input decomposition
Authors: Zhu, F. 
Guan, S.-U.
Keywords: Classification
Cooperative co-evolution
Genetic algorithms
Global fitness
Input decomposition
Local fitness
Issue Date: Dec-2008
Citation: Zhu, F., Guan, S.-U. (2008-12). Cooperative co-evolution of GA-based classifiers based on input decomposition. Engineering Applications of Artificial Intelligence 21 (8) : 1360-1369. ScholarBank@NUS Repository.
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 GA and normal GA. Some analysis and discussions on ECCGA and possible improvement are also presented. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Engineering Applications of Artificial Intelligence
ISSN: 09521976
DOI: 10.1016/j.engappai.2008.01.009
Appears in Collections:Staff Publications

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