Automated Algorithm Selection: Survey and Perspectives
Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann

TL;DR
This survey reviews the development and applications of automated algorithm selection, emphasizing its impact on solving diverse computational problems and discussing future challenges and opportunities.
Contribution
It provides a comprehensive overview of algorithm selection research, including applications to discrete and continuous problems, and discusses related approaches and feature extraction methods.
Findings
Significant improvements in solving combinatorial problems through algorithm selection.
Application of algorithm selection to continuous and mixed problems shows promising results.
Identification of open challenges and future directions in automated algorithm selection.
Abstract
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,…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · AI-based Problem Solving and Planning · Data Management and Algorithms
