Operational researchers and decision modelers have aspired to optimization technologies with a self-adaptive mechanism to cope with new problem formulations. Self-adaptive mechanisms not only free users from low-level and complex development tasks to enhance optimization efficiency but also allow them to focus on addressing high-level real-world operational requirements. In recent years, there has been a growing interest in applying machine learning and artificial intelligence techniques to improve self-adaptive mechanisms. However, learning to optimize hard combinatorial optimization problems remains a challenging task. This article proposes a new genetic programming approach to evolve efficient variable selectors to enhance the search mechanism in constraint programming. Starting with a set of training instances for a specific combinatorial optimization problem, the proposed approach evaluates variable selectors and evolves them to be more efficient over a number of generations. The novelties of our proposed approach are threefold: 1) a new representation of variable selectors; 2) a new mechanism for fitness evaluations; and 3) a preselection technique. We examine performance of the proposed approach on different job-shop scheduling problems, and the results show that variable selectors can be evolved efficiently. In particular, there are substantial reductions in the computational effort required for the search component of the constraint solver as well as increased chances of finding the optimal solutions. Further analyses also confirm the efficacy of our approach in respect to scalability, generalization, and interpretability of the evolved variable selectors.

中文翻译：

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约束规划中演化变量选择器的遗传编程方法


运筹学研究人员和决策建模者渴望具有自适应机制的优化技术来应对新的问题表述。自适应机制不仅可以将用户从低级复杂的开发任务中解放出来，提高优化效率，还可以让他们专注于解决高层的实际操作需求。近年来，人们对应用机器学习和人工智能技术来改进自适应机制越来越感兴趣。然而，学习优化硬组合优化问题仍然是一项具有挑战性的任务。本文提出了一种新的遗传编程方法来进化有效的变量选择器，以增强约束规划中的搜索机制。从特定组合优化问题的一组训练实例开始，所提出的方法评估变量选择器并使其在几代中变得更加高效。我们提出的方法有三个新颖之处：1）变量选择器的新表示； 2）适应度评估新机制； 3) 预选技术。我们检查了所提出的方法在不同作业车间调度问题上的性能，结果表明变量选择器可以有效地演化。特别是，约束求解器的搜索组件所需的计算工作量大大减少，并且找到最佳解决方案的机会也增加了。进一步的分析还证实了我们的方法在进化变量选择器的可扩展性、泛化性和可解释性方面的有效性。