Abstract
It is important to force search algorithms in promising regions of the solution space when solving large-scale problems. Genetic algorithms with search space reduction are proposed to solve flowshop problems. Additional precedence constraints generated by heuristic rules are introduced to reduce the search space of the genetic algorithm. An improved crossover operator which preserves the constraints is proposed and compared with other standard crossover operators. The computation results show that the algorithm has a significant improvement as compared with the standard genetic algorithms.
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