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Metaheuristics are often used to find solutions for optimization problems that are hard to solve exactly. A metaheuristic manipulates abstract solutions with the help of an operator to modify them in order to find the best solutions. We show that the efficiency of a solution encoding and its operator can vary with different metaheuristics and even with a metaheuristics according to different parameters. In this paper, we particularly compare tabu search with genetic algorithms. However, our results show that some encoding/operator pairs seems to have a consistent good behavior with these two metaheuristics.

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Efficiency Variations of Solution Encodings and Neighborhood Operators with Different Metaheuristics for the Job-Shop Scheduling Problem

  • 会议论文

  • 发表日期：

    2025年

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  • Karla Breschi,
  • Julien Bernard,
  • Hervé Manier,
  • Marie-Ange Manier

摘要

Metaheuristics are often used to find solutions for optimization problems that are hard to solve exactly. A metaheuristic manipulates abstract solutions with the help of an operator to modify them in order to find the best solutions. We show that the efficiency of a solution encoding and its operator can vary with different metaheuristics and even with a metaheuristics according to different parameters. In this paper, we particularly compare tabu search with genetic algorithms. However, our results show that some encoding/operator pairs seems to have a consistent good behavior with these two metaheuristics.