DocumentCode :
555514
Title :
An improvement diploid genetic algorithm for job-shop scheduling problem
Author :
Guo, Chen ; Huang, Ming ; Liang, Xu
Author_Institution :
Software Technol. Inst., Dalian Jiaotong Univ., Dalian, China
Volume :
Part 1
fYear :
2011
fDate :
3-5 Sept. 2011
Firstpage :
36
Lastpage :
38
Abstract :
Due to the complexity of JSP, there have some improvement. This paper brings up an improvement diploid genetic algorithm. This algorithm is based on the research which comes from GA. This method uses diploid dominant and recessive operation to inherit and retain the excellent individual genes. With the operation method of using convergence and dissimulation in MEC, the direction of evolution has been improved. The operation of dissimulation construct competes in different populations and the global research. This method improves the algorithm of total convergence and ability of overall research. The improvement algorithm overcomes the prematurity and the poor results of average fitness and genetic algorithm. The results of effectiveness have been shown in simulation experiment by using this algorithm.
Keywords :
genetic algorithms; job shop scheduling; JSP; diploid genetic algorithm; job-shop scheduling problem; recessive operation; Algorithm design and analysis; Biological cells; Convergence; Decoding; Encoding; Genetic algorithms; Job shop scheduling; double chromosomes; genetic algorithm; job-shop scheduling; mind evolutionary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-61284-446-6
Type :
conf
DOI :
10.1109/ICIEEM.2011.6035099
Filename :
6035099
Link To Document :
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