DocumentCode
2340802
Title
Notice of Retraction
A Self-Adaptive Genetic Algorithm Based on Real-Coded
Author
Jing Li ; Han Rui-feng
Author_Institution
Inf. Eng. Coll., Capital Normal Univ., Beijing, China
fYear
2010
fDate
23-25 April 2010
Firstpage
1
Lastpage
4
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The self-adaptive genetic algorithm, its main thought is to let control parameter (population, hybridization rate and mutation rate) adjusted adaptively within the proper range find the best parameter of the corresponding problem, thus received optimum which has stronger adaptability. It is proved that the self-adaptive genetic algorithm is with excellent convergence and higher precision than the traditional genetic algorithm through the comparison by optimizing four experimental functions.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The self-adaptive genetic algorithm, its main thought is to let control parameter (population, hybridization rate and mutation rate) adjusted adaptively within the proper range find the best parameter of the corresponding problem, thus received optimum which has stronger adaptability. It is proved that the self-adaptive genetic algorithm is with excellent convergence and higher precision than the traditional genetic algorithm through the comparison by optimizing four experimental functions.
Keywords
convergence; genetic algorithms; convergence; hybridization rate; mutation rate; self-adaptive genetic algorithm; Adaptive control; Convergence; Educational institutions; Evolution (biology); Genetic algorithms; Genetic engineering; Genetic mutations; Hybrid power systems; Optimal control; Programmable control;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5315-3
Type
conf
DOI
10.1109/ICBECS.2010.5462458
Filename
5462458
Link To Document