DocumentCode
2171317
Title
New genetic algorithm improved and its applications
Author
Xin, Zhao ; Chunbo, Xiu
Author_Institution
Sch. of Electr. Eng. & Autom., Tianjin Polytech. Univ., Tianjin, China
fYear
2011
fDate
9-11 Sept. 2011
Firstpage
926
Lastpage
928
Abstract
A novel genetic algorithm, named double population genetic algorithm (DPGA), is proposed to improve the performance of the conventional genetic algorithm. An elaborate searching space around the current optimal solution is divided from the original searching space. One small population executes genetic operators to speed up the convergence of the algorithm in the elaborate searching space. And the boundaries of the elaborate searching space are reduced continuously to enhance the searching density during the optimization. Another big population executes genetic operators to ensure the global optimal ability of the algorithm. In this way, the algorithm has global searching ability and fast convergence rate. A lot of simulation results prove that the algorithm can accelerate searching rate, enhance the searching efficiency, and give satisfied results to function optimization problems.
Keywords
convergence; genetic algorithms; double population genetic algorithm; elaborate searching space; fast convergence rate; function optimization problem; genetic operators; global optimal ability; global searching ability; optimal solution; Algorithm design and analysis; Convergence; Educational institutions; Genetic algorithms; Genetics; Optimization; Search problems; elaborate searching space; function optimization; genetic algorithm; population;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronics, Communications and Control (ICECC), 2011 International Conference on
Conference_Location
Ningbo
Print_ISBN
978-1-4577-0320-1
Type
conf
DOI
10.1109/ICECC.2011.6066413
Filename
6066413
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