Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can help replace tedious and labor-intensive manual selection, and have already shown promising performance in various optimization domains. Automated AS relies on machine learning (ML) techniques to recommend the best algorithm given the information about the problem instance. Unfortunately, there are no clear guidelines for choosing the most appropriate one from a variety of ML techniques. Tree-based models such as Random Forest or XGBoost have consistently demonstrated outstanding performance for automated AS. Transformers and other tabular deep learning models have also been increasingly applied in this context. We investigate in this work the impact of the choice of the ML technique on AS performance. We compare four ML models on the task of predicting the best solver for the BBOB problems for 7 different runtime budgets in 2 dimensions. While our results confirm that a per-instance AS has indeed impressive potential, we also show that the particular choice of the ML technique is of much minor importance.

翻译：用于解决黑箱优化问题的求解器之间存在性能互补性，这使得算法选择（AS）成为一项重要任务。自动化AS方法有助于取代繁琐且劳动密集型的人工选择，并在多个优化领域展现出良好性能。自动化AS依赖机器学习（ML）技术，根据问题实例的信息推荐最优算法。然而，目前缺乏从多种ML技术中选择最合适方法的明确指南。诸如随机森林或XGBoost等基于树的模型在自动化AS中始终表现出色。Transformer及其他表格深度学习模型在该领域的应用也日益增多。本研究探讨了ML技术选择对AS性能的影响。我们比较了四种ML模型在BBOB问题上预测最佳求解器的能力，涉及7种不同运行时间预算和2个维度。尽管结果证实实例级AS具有显著潜力，但我们也表明，ML技术的具体选择对整体性能的影响相对次要。