It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

中文翻译：

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自动算法选择：调查和观点

长期以来，人们一直观察到，对于几乎任何已经深入研究的计算问题，使用不同的算法可以最好地解决不同的实例。这对于计算困难的问题尤其明显，在大多数情况下，没有单一算法定义最先进的技术；相反，有一组具有互补优势的算法。可以通过多种方式利用这种性能互补性，其中一种方式是基于从一组给定算法中为每个要解决的问题实例选择预期性能最好的实例的想法。从给定集合中自动选择算法的任务被称为逐实例算法选择问题，并且在过去的 15 年中得到了深入研究，导致在解决越来越多的离散组合问题（包括命题可满足性和 AI 规划）方面的最新技术水平的重大改进。每个实例的算法选择也显示出在解决连续和混合离散/连续优化问题时提高性能的很大希望。该调查概述了自动算法选择的研究，从早期和开创性的工作到最近和有前途的应用领域。与早期的工作不同，它涵盖了离散和连续问题的应用，并在上下文中讨论了算法选择与概念相关的方法，例如算法配置、调度或投资组合选择。由于信息丰富且可计算的问题实例特征为有效的逐实例算法选择系统提供了基础，因此我们还概述了离散和连续问题的此类特征。最后，我们提供了对该领域未来工作的看法，并讨论了一些开放的研究挑战。