%0 Journal Article %1 Kerschke2019-lf %A Kerschke, Pascal %A Hoos, Holger H %A Neumann, Frank %A Trautmann, Heike %D 2019 %I MIT Press %J Evol. Comput. %K Automated algorithm analysis; approaches; automated combinatorial configuration; continuous data exploratory feature-based landscape learning; machine metalearning optimisation; selection; streams.; %N 1 %P 3--45 %T Automated algorithm selection: Survey and perspectives %V 27 %X 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.

@article{Kerschke2019-lf, 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, 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.}, added-at = {2024-09-10T11:54:51.000+0200}, author = {Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2dea7f7c7d4b8fdf689b5e01162e0af6a/scadsfct}, interhash = {450c6c93d207e0ea860130c45effb36c}, intrahash = {dea7f7c7d4b8fdf689b5e01162e0af6a}, journal = {Evol. Comput.}, keywords = {Automated algorithm analysis; approaches; automated combinatorial configuration; continuous data exploratory feature-based landscape learning; machine metalearning optimisation; selection; streams.;}, language = {en}, number = 1, pages = {3--45}, publisher = {MIT Press}, timestamp = {2024-09-10T11:54:51.000+0200}, title = {Automated algorithm selection: Survey and perspectives}, volume = 27, year = 2019 }