English(EN) Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation

新的 Weisfeiler-Leman 特征增强了约束编程中的算法选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种新的约束编程算法选择方法，该方法利用 Weisfeiler-Leman (WL) 特征。这种方法实现了特征提取的自动化，超越了传统的手动统计方法，能够捕捉底层问题结构。提出的基于割的表示（称为 WLc）对结构分区进行建模，以提供更细致的预测信号。在 MiniZinc Challenge 实例上使用支持向量机、随机森林和多层感知器进行的实验表明，WLc 特征的性能优于现有方法。 AI

影响引入了一种新颖的约束编程自动化特征提取方法，有望提高效率和预测准确性。

排序理由该集群包含一篇研究论文，详细介绍了用于约束编程的机器学习新颖特征提取方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Alessio Pellegrino, Jacopo Mauro · 2026-10-09 04:00

利用Weisfeiler-Leman特征进行约束优化中的算法选择

arXiv:2610.12119v1 Announce Type: cross Abstract: Algorithm Selection is essential for efficient Constraint Programming. Over the years, many algorithm selectors based on machine learning methods have been successfully applied, yet traditional feature extraction methods often rel…

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利用Weisfeiler-Leman特征进行约束优化中的算法选择

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