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- W2374934203 abstract "In order to verify the optimal sub-population genetic algorithm is better than traditional genetic algorithm in solving the flexible flow shop scheduling problem,this paper analyzed the characteristics of the flexible flow shop scheduling problem,and applied a new coding method and the new genetic algorithm to solve the problem.This paper considered the protection strategy method of optimal individual made populations easy to converge to a local optimal solution in solving complex issues.In order to improve accuracy,speed up the production of better individuals and avoid falling into local optimal solution,this paper proposed a rational,comprehensive encoding method and used optimal sub-population genetic algorithm to solve flexible flow shop scheduling problem.Finally,the use of examples verifies the effectiveness and superiority of optimal sub-population genetic algorithm and the rationality of coding." @default.
- W2374934203 created "2016-06-24" @default.
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- W2374934203 date "2012-01-01" @default.
- W2374934203 modified "2023-09-23" @default.
- W2374934203 title "Optimal sub-population genetic algorithm for flexible flow shop scheduling problem" @default.
- W2374934203 hasPublicationYear "2012" @default.
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