DocumentCode
518672
Title
Notice of Retraction
Research on genetic algorithm based on oriented weighted crossover operator
Author
Zhou Yu-heng ; Xie Wei-da
Author_Institution
Inst. of Railway & Urban Rail Transit, Tongji Univ., Shanghai, China
Volume
1
fYear
2010
fDate
27-29 March 2010
Firstpage
10
Lastpage
13
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.
Crossover is the most important step in genetic algorithm, which passes on good genes of the parents to the next generation. However, the crossover operator can´t ensure the offspring finding the globally optimum because of its blind search. A weighted crossover operator is proposed to overcome the disadvantages. Weighted values are calculated according to the adaptive values of the parents. One offspring is generated through the weighted crossover operator. On this basis, an oriented weighted crossover operator is proposed to increase the probability of finding excellent solution genes. The convergence of the genetic algorithm based on this crossover operator is proved. Experiments show that the genetic algorithm based on this operator has a good performance in searching optimum.
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.
Crossover is the most important step in genetic algorithm, which passes on good genes of the parents to the next generation. However, the crossover operator can´t ensure the offspring finding the globally optimum because of its blind search. A weighted crossover operator is proposed to overcome the disadvantages. Weighted values are calculated according to the adaptive values of the parents. One offspring is generated through the weighted crossover operator. On this basis, an oriented weighted crossover operator is proposed to increase the probability of finding excellent solution genes. The convergence of the genetic algorithm based on this crossover operator is proved. Experiments show that the genetic algorithm based on this operator has a good performance in searching optimum.
Keywords
genetic algorithms; genetic algorithm; oriented weighted crossover operator; weighted values; Arithmetic; Cost function; Genetic algorithms; Genetic mutations; Rail transportation; Adaptive Value; Genetic Algorithm; Orientation; Weighted Crossover Operator;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5486769
Filename
5486769
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