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.

翻译：算法选择旨在为给定问题实例选取最合适的算法，其核心驱动力在于利用不同算法在问题实例集合上的互补性能。然而，为未见问题实例确定最优算法已被证明是一项具有挑战性的任务，近年来引起了研究者的广泛关注。本综述系统梳理了单目标连续黑盒优化领域中算法选择的关键贡献。我们介绍了当前在优化问题实例、算法实例及其交互关系的元特征表示学习方面的研究进展，并探讨了用于自动化算法选择、配置与性能预测的机器学习模型。通过分析现有研究，我们指出了当前技术水平的不足，并在此基础上提出了元特征表示进一步发展的思路。