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
