In black-box optimization, a central question is which algorithm to use to solve a given, previously unseen, problem. Selecting a single algorithm, however, entails inherent risks: inaccuracies in the selector may lead to poor choices, and even well-performing algorithms with high variance can yield unsatisfactory results in a single run. A natural remedy is to split the evaluation budget across multiple runs of potentially different algorithms. Such sequential algorithm portfolios benefit from variance reduction and complementarities between algorithms, often outperforming approaches that allocate the entire budget to a single solver. While effective portfolios can be constructed post-hoc, transferring this idea to the algorithm selection setting is non-trivial. We show that a naive portfolio constructed over the full training set already outperforms the strongest traditional baseline, the virtual best solver. We then propose a simple yet effective k-nearest-neighbor-based finetuning approach to construct portfolios tailored to unseen instances, yielding further improvements and highlighting the effectiveness of portfolio selection in fixed-budget black-box optimization.

翻译：在黑箱优化中，一个核心问题是如何为给定的、未见过的任务选择合适的算法。然而，仅选择单一算法存在固有风险：选择器的不准确可能导致次优选择，即使表现良好但方差较大的算法在单次运行中也可能产生不理想的结果。一种自然的解决方案是将评估预算分配给多个可能不同算法的多次运行。这种序贯算法组合能够利用方差缩减和算法间的互补性，通常优于将全部预算分配给单个求解器的方法。尽管可以在事后构建有效的组合，但将该思路迁移到算法选择场景并不简单。我们证明，在全训练集上朴素构建的组合已超越最强的传统基线——虚拟最佳求解器。随后，我们提出一种简单而有效的基于k近邻的微调方法，为未见实例量身定制组合，进一步提升了性能，凸显了组合选择在固定预算黑箱优化中的有效性。