A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.
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.
Hajri
S
S
Ecole Nat. d'Ingenieurs de Monastir.
Liouane
N
N
Hammadi
S
S
Borne
P
P