DocumentCode
2626672
Title
A Genetic Ant Colony Classifier System
Author
Zhang, Y.D. ; Wu, L.N.
Author_Institution
Sch. of Inf. Sci. & Eng., Southeast Univ., China
Volume
5
fYear
2009
fDate
March 31 2009-April 2 2009
Firstpage
744
Lastpage
748
Abstract
The genetic algorithm (GA) has been widely applied as a soft computing technique in various fields, while the ant colony algorithm (ACA) is a rapidly developing tool used for optimization. Based on the combination of the fast global search ability of GA and the positive feedback mechanism of ACO, a novel algorithm, named genetic ant colony algorithm (GACA) was proposed in the domain of pattern classification. Experiments show that the classifier based on GACA can achieve better performance than that the normal GA and ACA does.
Keywords
genetic algorithms; pattern classification; probability; search problems; feedback mechanism; genetic ant colony classifier; global search ability; optimization; pattern classification; probability; redundant; soft computing technique; Ant colony optimization; Computer science; Data mining; Encoding; Feedback; Genetic algorithms; Genetic engineering; Information science; Neural networks; Pattern classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location
Los Angeles, CA
Print_ISBN
978-0-7695-3507-4
Type
conf
DOI
10.1109/CSIE.2009.748
Filename
5170632
Link To Document