DocumentCode
2296148
Title
Notice of Retraction
Mixed-coding Differential Evolution Algorithm for Integer Programming
Author
Changshou Deng ; Bingyan Zhao ; Anyuan Deng ; Changyong Liang
Author_Institution
Sch. of Inf. Sci. & Technol., Jiujiang Univ., Jiujiang, China
Volume
1
fYear
2010
fDate
6-7 March 2010
Firstpage
79
Lastpage
81
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.
Mixed-coding population Differential Evolution algorithm was proposed for integer programming. Firstly, each individual in the initial population with two different fields, one is real and the other is integer, is generated. Then a new operator, boundary-handling operator, was added to the original Differential Evolution to ensure each population generated by the mutation operator and crossover operator satisfying the boundary constraints. Lastly a new mapping operator was put forward to map one real number into an integer number, which is stored in the integer field. Two initial simulation results of two integer programming with the size of 30 variables show it is effective and efficient. Mixed-coding Differential Evolution algorithm is a new effective way for solving the integer programming problems.
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.
Mixed-coding population Differential Evolution algorithm was proposed for integer programming. Firstly, each individual in the initial population with two different fields, one is real and the other is integer, is generated. Then a new operator, boundary-handling operator, was added to the original Differential Evolution to ensure each population generated by the mutation operator and crossover operator satisfying the boundary constraints. Lastly a new mapping operator was put forward to map one real number into an integer number, which is stored in the integer field. Two initial simulation results of two integer programming with the size of 30 variables show it is effective and efficient. Mixed-coding Differential Evolution algorithm is a new effective way for solving the integer programming problems.
Keywords
evolutionary computation; integer programming; boundary-handling operator; crossover operator; integer programming; mixed-coding population differential evolution algorithm; mutation operator; Cities and towns; Computer science; Computer science education; Educational programs; Educational technology; Genetic mutations; Information science; Linear programming; Signal processing algorithms; Space exploration; Integer Programming; Mixed-coding Differential Evolution; boundary-handling operator; mapping operator;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.263
Filename
5459621
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