DCL-GPGLS：大规模车辆路径问题中遗传编程引导局部搜索的动态课程学习

中文摘要 AI 辅助

遗传编程引导局部搜索（GPGLS）利用遗传编程为大规模车辆路径问题（LSVRPs）中的引导局部搜索演化效用函数。在每一代中对每个训练实例评估每个遗传编程个体代价高昂，因此GPGLS通常在小批量实例上进行训练。现有的基于课程的GPGLS主要按实例规模对这些批次进行排序。自适应课程学习GPGLS（ACL-GPGLS）通过调整搜索在固定课程阶段之间的移动时机来提高训练效率，但实例难度顺序仍是预定义的。我们提出DCL-GPGLS，它根据当前种群解的质量估计每个训练实例的难度，并在演化过程中更新这些估计。每一代随后接收一个接近计划难度水平的批次，并带有修正以避免重复选择相同实例。在CVRPLIB X集的固定训练-测试划分上的实验表明，在六种训练策略中，DCL-GPGLS取得了最佳的平均排名和平均测试成本。它在65个未见测试实例中的36个上获得最低平均成本，并且在总评估器调用次数匹配的情况下，在6个实例上显著优于静态反馈推导的课程，在其余59个实例上无显著差异。

英文摘要

Genetic Programming Guided Local Search (GPGLS) uses genetic programming to evolve utility functions for guided local search in large-scale vehicle routing problems (LSVRPs). Evaluating every GP individual on every training instance at every generation is expensive, so GPGLS is usually trained on small instance batches. Existing curriculum-based GPGLS orders these batches mainly by instance size. Adaptive Curriculum Learning GPGLS (ACL-GPGLS) improves training efficiency by adapting when the search moves between fixed curriculum stages, but the instance difficulty order remains predefined. We propose DCL-GPGLS, which estimates the difficulty of each training instance from the current population's solution quality and updates the estimates during evolution. Each generation then receives a batch near a scheduled difficulty level, with a correction that limits repeated selection of the same instances. Experiments on a fixed training-test split of the CVRPLIB X set show that DCL-GPGLS achieves the best observed average rank and mean test cost among six training policies. It obtains the lowest mean cost on 36 of 65 unseen test instances and is significantly better than the static feedback-derived curriculum, matched in total evaluator calls, on 6 instances, with no significant difference on the remaining 59.

发表机构

• School of Engineering and Computer Science, Victoria University of Wellington（惠灵顿维多利亚大学工程与计算机科学学院）

机构由 AI 辅助整理，请以论文原文为准。

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14 pages, 3 figures