Cooperative co-evolution of GA-based classifiers based on input decomposition

Authors:
Fangming Zhu;Sheng-Uei Guan
Affiliations:
Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore;School of Engineering and Design, Brunel University, Uxbridge, Middlesex UB8 3PH, UK
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

Citing 13
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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.