DocumentCode
2031884
Title
Sparse optimization using a mixed GA-PSO optimization framework
Author
Dong, Ruijun ; Pedrycz, Witold
Author_Institution
Dept. of Autom., Xidian Univ., Xi´´an, China
Volume
4
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
1862
Lastpage
1866
Abstract
Evolutionary optimizers (EOs) have assumed a visible position as important problem solvers because of their flexibility, versatility, and ability to optimize in complex multimodal search spaces. This paper discusses a problem of sparse optimization with a special emphasis placed on mixed genetic algorithm-particle swarm optimization (GA-PSO) techniques.
Keywords
genetic algorithms; particle swarm optimisation; search problems; complex multimodal search spaces; evolutionary optimizers; genetic algorithm; mixed GA-PSO optimization framework; particle swarm optimization; sparse optimization; Accuracy; Algorithm design and analysis; Artificial neural networks; Feature extraction; Optimization; Particle swarm optimization; Polynomials; mixed evolutionary approaches; optimization; sparse;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-5931-5
Type
conf
DOI
10.1109/FSKD.2010.5569440
Filename
5569440
Link To Document