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A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling

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4 Author(s)
Hajri, S. ; Ecole Nat. d''Ingenieurs de Monastir, Tunisia ; Liouane, N. ; Hammadi, S. ; Borne, P.

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:30 ,  Issue: 5 )