DocumentCode
2133644
Title
Notice of Retraction
Research of University Timetable Problem based on genetic algorithm
Author
Wang, Cheng ; Wang, Shi-bo ; Wang, Tie
Author_Institution
Department of Management Science and Engineering, Qiqihar University, China
fYear
2010
fDate
4-6 Dec. 2010
Firstpage
5294
Lastpage
5297
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.
Take the University Timetable Problem (UTP) as the research object, this paper divide the University Timetable Problem into two aspects: the determination of basic teaching tasks and the optimization of basic teaching time, Clarify the task of basic teaching methods and processes, Discusses in detail the steps of optimizing the basic teaching time based on genetic algorithm, Gives the algorithm of the fitness function, genetic crossover, variation and generation of initial population, and verified the value of the algorithm.
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.
Take the University Timetable Problem (UTP) as the research object, this paper divide the University Timetable Problem into two aspects: the determination of basic teaching tasks and the optimization of basic teaching time, Clarify the task of basic teaching methods and processes, Discusses in detail the steps of optimizing the basic teaching time based on genetic algorithm, Gives the algorithm of the fitness function, genetic crossover, variation and generation of initial population, and verified the value of the algorithm.
Keywords
Complexity theory; Computer applications; Computers; Education; Genetics; Optimization; UTP; cross-algorithm; genetic algorithm; practical;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2010 2nd International Conference on
Conference_Location
Hangzhou, China
Print_ISBN
978-1-4244-7616-9
Type
conf
DOI
10.1109/ICISE.2010.5690628
Filename
5690628
Link To Document