Tang Ke, Yao Xin
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Department of Computing and Decision Sciences, Lingnan University, Hong Kong 999077, China.
Natl Sci Rev. 2024 Apr 2;11(8):nwae132. doi: 10.1093/nsr/nwae132. eCollection 2024 Aug.
Most optimization problems of practical significance are typically solved by highly configurable parameterized algorithms. To achieve the best performance on a problem instance, a trial-and-error configuration process is required, which is very costly and even prohibitive for problems that are already computationally intensive, e.g. optimization problems associated with machine learning tasks. In the past decades, many studies have been conducted to accelerate the tedious configuration process by learning from a set of training instances. This article refers to these studies as and reviews the progress achieved.
大多数具有实际意义的优化问题通常由高度可配置的参数化算法来解决。为了在一个问题实例上实现最佳性能，需要一个反复试验的配置过程，这对于已经计算密集的问题来说成本非常高，甚至是难以承受的，例如与机器学习任务相关的优化问题。在过去几十年里，已经进行了许多研究，通过从一组训练实例中学习来加速这个繁琐的配置过程。本文将这些研究称为 并回顾所取得的进展。
