Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an analysis phase, e.g. where features are computed by sampling solutions and used as input in a machine-learning model. For AS to be efficient, it is therefore important that this analysis phase is not computationally expensive. We propose a method for identifying easy instances which can be solved quickly using a generalist solver without any need for algorithm-selection. This saves computational budget associated with feature-computation which can then be used elsewhere in an AS pipeline, e.g., enabling additional function evaluations on hard problems. Experiments on the BBOB dataset in two settings (batch and streaming) show that identifying easy instances results in substantial savings in function evaluations. Re-allocating the saved budget to hard problems provides gains in performance compared to both the virtual best solver (VBS) computed with the original budget, the single best solver (SBS) and a trained algorithm-selector.

翻译：算法选择方法对于在大量实例上从求解器组合中获得最佳性能至关重要。然而，许多算法选择方法依赖于分析阶段，例如通过采样解计算特征并将其用作机器学习模型的输入。为确保算法选择的高效性，该分析阶段的计算开销不应过高。本文提出一种识别易解实例的方法，这些实例可直接使用通用求解器快速求解，无需进行算法选择。这节省了特征计算相关的计算资源，从而可将其重新分配至算法选择流程的其他环节，例如用于困难问题的额外函数评估。在BBOB数据集上进行的批量与流式两种设置下的实验表明，识别易解实例能显著减少函数评估次数。将节省的计算资源重新分配给困难问题后，相较于原始预算下的虚拟最佳求解器、单一最佳求解器以及训练后的算法选择器，均能获得性能提升。