动态多模式项目调度中基于表型特征表征的代理辅助遗传规划
Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling
- Victoria University of Wellington（惠灵顿维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对动态多模式项目调度，提出代理辅助遗传规划结合表型特征表征，通过二进制编码与欧氏距离有效估计适应度，并控制冗余、保留多样性，提升启发式质量。
AI中文摘要：
动态多模式资源受限项目调度需要在优先约束、有限资源、多种执行模式和不确定活动工期下做出决策。遗传规划（GP）可以自动为这类问题演化启发式规则，但其基于仿真的适应度评估计算成本高昂。本研究探讨了在代理辅助GP中采用表型特征表征（PC），以在固定预算的全仿真适应度评估下演化更高质量的调度启发式。一个关键问题是GP个体应如何编码为表型特征表征以支持有效的适应度估计。为回答此问题，设计了三种信息丰富度不同的PC编码方案：优先级值编码，保留原始规则输出；排名编码，捕获候选排序；以及二进制编码，表示最终调度决策。这些编码与不同的距离度量相结合，以衡量GP个体之间的行为相似性。实验结果表明，采用欧氏距离的二进制编码提供了最有效且稳健的代理指导。进一步分析表明，仅代理估计精度并不能完全解释性能差异。PC表示还决定了表型冗余后代被移除的有效程度，以及在预选择后保留了多少行为多样性。消融实验进一步证明，去重和代理预选择提供了互补的益处，且两者结合产生了最大的改进。这些发现强调，有效的代理辅助GP不仅依赖于识别有前景的后代，还依赖于在进化搜索过程中控制冗余并保留有用的多样性。
英文摘要：
Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple execution modes, and uncertain activity durations. Genetic programming (GP) can automatically evolve heuristic rules for such problems, but its simulation-based fitness evaluation is computationally expensive. This study investigates phenotypic characterisation (PC) in surrogate-assisted GP to evolve higher-quality scheduling heuristics under a fixed budget of full simulation-based fitness evaluations. A key question is how GP individuals should be encoded into phenotypic characterisations to support effective fitness estimation. To answer this question, three PC encoding schemes with different levels of information richness are designed: priority-value encoding, which preserves raw rule outputs; rank encoding, which captures candidate ordering; and binary encoding, which represents final scheduling decisions. These encodings are combined with different distance metrics to measure behavioural similarity between GP individuals. The experimental results show that binary encoding with Euclidean distance provides the most effective and robust surrogate guidance. Further analyses show that surrogate estimation accuracy alone does not fully explain the performance differences. The PC representation also determines how effectively phenotypically redundant offspring are removed and how much behavioural diversity is retained after preselection. Ablation experiments further demonstrate that duplicate removal and surrogate preselection provide complementary benefits, with their combination producing the largest improvement. These findings highlight that effective surrogate-assisted GP depends not only on identifying promising offspring, but also on controlling redundancy and preserving useful diversity during evolutionary search.
