The selection of the most appropriate algorithm to solve a given problem
instance, known as algorithm selection, is driven by the potential to
capitalize on the complementary performance of different algorithms across sets
of problem instances. However, determining the optimal algorithm for an unseen
problem instance has been shown to be a challenging task, which has garnered
significant attention from researchers in recent years. In this survey, we
conduct an overview of the key contributions to algorithm selection in the
field of single-objective continuous black-box optimization. We present ongoing
work in representation learning of meta-features for optimization problem
instances, algorithm instances, and their interactions. We also study machine
learning models for automated algorithm selection, configuration, and
performance prediction. Through this analysis, we identify gaps in the state of
the art, based on which we present ideas for further development of
meta-feature representations.

通过对单目标连续黑箱优化领域的算法选择的关键贡献进行概述，我们介绍了在优化问题实例、算法实例及其交互的元特征表示学习方面的正在进行的工作，并研究了用于自动算法选择、配置和性能预测的机器学习模型，通过这些分析，我们发现了现有技术的不足之处，并提出了进一步发展元特征表示的思路。