Benchmarking Feature-Based Algorithm Selection Systems for Black-Box Numerical Optimization
本文分析了24个无噪声黑箱优化基准函数上的算法选择系统，发现首次成功性能指标比预期运行时间更可靠，并指出超越单一最优求解器的难度取决于算法组合、交叉验证方法和维度。
Feature-based algorithm selection aims to automatically find the best one from a portfolio of optimization algorithms on an unseen problem based on its landscape features. Feature-based algorithm selection has recently received attention in the research field of black-box numerical optimization. However, there is still room for the analysis of algorithm selection for black-box optimization. Most previous studies have focused only on whether an algorithm selection system can outperform the single-best solver (SBS) in a portfolio. In addition, a benchmarking methodology for algorithm selection systems has not been well investigated in the literature. In this context, this article analyzes algorithm selection systems on the 24 noiseless black-box optimization benchmarking functions. First, we demonstrate that the first successful performance measure is more reliable than the expected runtime measure for benchmarking algorithm selection systems. Then, we examine the influence of randomness on the performance of algorithm selection systems. We also show that the performance of algorithm selection systems can be significantly improved by using sequential least squares programming as a presolver. We point out that the difficulty of outperforming the SBS depends on algorithm portfolios, cross-validation methods, and dimensions. Finally, we demonstrate that the effectiveness of algorithm portfolios depends on various factors. These findings provide fundamental insights for algorithm selection for black-box optimization.