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。AI 中文总结
针对GPGLS学习车辆路径问题引导局部搜索时仅靠适应度管理多样性不足的问题，提出基于行为小生境的BN-GPGLS，用算子级描述符表征程序并选档案父代，实验表明BN-Adaptive排名最优且能减小程序规模。
AI 中文摘要
遗传规划引导局部搜索（GPGLS）学习用于引导车辆路径问题中局部搜索的效用函数。其演化程序可能具有相似的适应度，但会引发不同的搜索行为，这使得仅凭适应度不足以作为种群多样性管理的完整基础。我们提出了基于行为小生境的GPGLS（BN-GPGLS），该方法通过局部搜索过程中收集的六个算子级描述符来表征程序。当前代档案从行为得分的分层中选择具有适应度竞争力且紧凑的代表。固定策略持续使用档案父代，而自适应策略则利用训练适应度和标准化行为离散度信号（可选地结合树大小条件）来激活它们。我们在生成的200个客户实例上，将四种基于行为的变体与无档案GPGLS对照及基于适应度的小生境方法进行了30次种子匹配运行比较。BN-Adaptive在独立的90实例监控集上取得了最佳描述性平均排名；聚合路由成本差异较小。所有五种档案策略产生的最终种群中位数树大小均小于GPGLS对照，配对Wilcoxon检验在Holm调整后仍保持显著。这些结果在评估设置中识别出有用的解质量和程序大小权衡，但并未将大小缩减单独归因于行为表示。
英文摘要
Genetic Programming Guided Local Search (GPGLS) learns utility functions that guide local search for vehicle routing. Its evolving programs can have similar fitness while inducing different search behaviour, making fitness alone an incomplete basis for population diversity management. We propose GPGLS with Behaviour-based Niching (BN-GPGLS), which characterises programs through six operator-level descriptors collected during local search. A current-generation archive selects fitness-competitive, compact representatives from strata of a behaviour score. Fixed policies use archive parents continuously, whereas adaptive policies activate them using training-fitness and standardised behaviour-dispersion signals, optionally with a tree-size condition. We compare four behaviour-based variants with a no-archive GPGLS control and fitness-based niching over 30 seed-matched runs on generated 200-customer instances. BN-Adaptive achieves the best descriptive average rank on a separate 90-instance monitoring set; aggregate routing-cost differences are small. All five archive policies produce lower final-population median tree sizes than the GPGLS control, with paired Wilcoxon comparisons remaining significant after Holm adjustment. These results identify useful solution-quality and program-size trade-offs within the evaluated setting, without attributing the size reductions to behaviour representation alone.
Comments15 pages, 3 figures
