Selector: Ensemble-Based Automated Algorithm Configuration
Solvers contain parameters that influence their performance and these must be set by the user to ensure that high-quality solutions are generated, or optimal solutions are found quickly. Manually setting these parameters is tedious and error-prone, since search spaces may be large or even infinite....
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| Veröffentlicht in: | Journal of heuristics Jg. 31; H. 3; S. 28 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
Springer US
01.09.2025
Springer Nature B.V |
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| ISSN: | 1572-9397, 1381-1231, 1572-9397 |
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| Abstract | Solvers contain parameters that influence their performance and these must be set by the user to ensure that high-quality solutions are generated, or optimal solutions are found quickly. Manually setting these parameters is tedious and error-prone, since search spaces may be large or even infinite. Existing approaches to automate the task of algorithm configuration (AC) make use of a single machine learning model that is trained on previous runtime data and used to create or evaluate promising new configurations. We combine a variety of successful models from different AC approaches into an ensemble that proposes new configurations. To this end, each model in the ensemble suggests configurations and a hyper-configurable selection algorithm chooses a subset of configurations to match the amount of computational resources available. We call this approach Selector , and we examine its performance against the state-of-the-art AC methods PyDGGA and SMAC, respectively. The new configurator will be made available as an open source software package. |
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| AbstractList | Solvers contain parameters that influence their performance and these must be set by the user to ensure that high-quality solutions are generated, or optimal solutions are found quickly. Manually setting these parameters is tedious and error-prone, since search spaces may be large or even infinite. Existing approaches to automate the task of algorithm configuration (AC) make use of a single machine learning model that is trained on previous runtime data and used to create or evaluate promising new configurations. We combine a variety of successful models from different AC approaches into an ensemble that proposes new configurations. To this end, each model in the ensemble suggests configurations and a hyper-configurable selection algorithm chooses a subset of configurations to match the amount of computational resources available. We call this approach Selector , and we examine its performance against the state-of-the-art AC methods PyDGGA and SMAC, respectively. The new configurator will be made available as an open source software package. |
| ArticleNumber | 28 |
| Author | Schede, Elias Weiß, Dimitri Tierney, Kevin |
| Author_xml | – sequence: 1 givenname: Dimitri surname: Weiß fullname: Weiß, Dimitri – sequence: 2 givenname: Elias surname: Schede fullname: Schede, Elias – sequence: 3 givenname: Kevin surname: Tierney fullname: Tierney, Kevin |
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| Cites_doi | 10.1007/978-3-030-80223-3_2 10.1007/978-3-030-92121-7_2 10.1016/j.ejor.2013.10.043 10.1016/j.eswa.2008.01.039 10.1016/j.artint.2016.04.003 10.1007/s10994-022-06161-4 10.1007/978-3-030-72013-1_16 10.1016/j.eswa.2023.121674 10.1613/jair.2861 10.1007/978-3-031-24866-5_13 10.1609/aaai.v31i1.10660 10.1007/978-3-642-25566-3_40 10.1007/s43069-024-00327-7 10.1145/3377930.3390211 10.1109/4235.585893 10.1016/j.orp.2016.09.002 10.1007/978-3-642-04244-7_14 10.1007/978-3-319-09584-4_4 10.1162/evco_a_00242 10.1007/978-3-642-33558-7_11 10.1109/MCAS.2006.1688199 10.1016/j.ejor.2015.08.018 10.1007/978-3-030-53552-0_22 10.1613/jair.1.13676 |
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| SubjectTerms | Algorithm configuration Algorithms Automation Configurations CVRP Ensemble optimization Genetic algorithms Integer programming Machine learning MAX-SAT Methods MILP Open source software Optimization Parameters SAT TSP |
| Title | Selector: Ensemble-Based Automated Algorithm Configuration |
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