Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2008.01.009
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
Source: 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. https://doi.org/10.1016/j.engappai.2008.01.009
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
URI: http://scholarbank.nus.edu.sg/handle/10635/129633
ISSN: 09521976
DOI: 10.1016/j.engappai.2008.01.009
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

19
checked on Feb 15, 2018

WEB OF SCIENCETM
Citations

12
checked on Feb 5, 2018

Page view(s)

10
checked on Feb 13, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.