论文标题
Sunny-AS2：增强阳光以选择算法
sunny-as2: Enhancing SUNNY for Algorithm Selection
论文作者
论文摘要
Sunny是最初针对约束编程（CP）量身定制的算法选择（AS）。 Sunny使得可以从求解器的投资组合进行安排，该子集可以在给定的CP问题上运行。事实证明，这种方法对CP问题有效，其并行版本赢得了Minizinc Challenge的公开类别的许多金牌 - CP求解器的年度国际竞赛。在2015年，Aslib基准被发布，以将来自不同领域的系统（例如ASP，QBF和SAT）进行比较，并扩展了Sunny，以处理通用问题。这导致了Sunny-AS2的开发，Sunny-AS2是一种基于Aslib场景的算法选择器。 Sunny-AS2的初步版本已在2017年提交了Open算法选择挑战（OASC），事实证明，它是决策问题最小化运行时的最佳方法。在这项工作中，我们介绍了Sunny-AS2的技术进步，包括：（i）基于包装的功能选择； （ii）一种结合特征选择和邻里尺寸配置的训练方法； （iii）嵌套交叉验证的应用。我们展示了Sunny-AS2的性能如何根据所考虑的场景而变化，并讨论其优势和劣势。最后，我们还展示了Sunny-AS2在提交给OASC的初步版本上如何改进。
SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC.
