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

Fangming Zhu*, Sheng Uei Guan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1360-1369
Number of pages10
JournalEngineering Applications of Artificial Intelligence
Issue number8
Publication statusPublished - Dec 2008
Externally publishedYes


  • Classification
  • Cooperative co-evolution
  • Genetic algorithms
  • Global fitness
  • Input decomposition
  • Local fitness

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