Automated Algorithm Selection: Survey and Perspectives

By Pascal Kerschke, Holger H. Hoos, F. Neumann, Heike Trautmann. Evolutionary Computation, 2018

477 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/be1fa570187f20c03b97fa0e9b09f44e73eb33c2

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Citations in the corpus

Learning Performance-Improving Code Edits (2023, methodology): “…2022; Kumar et al., 2022), auto-vectorize code (Nuzman et al., 2006; Mendis et al., 2019), optimize GPU code (Liou et al., 2020; Cummins et al., 2021), and automatically select algorithms (Kotthoff, 2016; Kerschke et al., 2019). and room at the top (Leiserson et al., 2020; Sherry & Thompson, 2021).”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “Regarding the problem complexity, if this is remarkably high, automated algorithm selection mechanisms can be an appropriate alternative [83].”