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: Weiß, Dimitri, Schede, Elias, Tierney, Kevin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY Springer US 01.09.2025
Springer Nature B.V
Schlagworte:
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
ISSN:1572-9397, 1381-1231, 1572-9397
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Zusammenfassung: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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ISSN:1572-9397
1381-1231
1572-9397
DOI:10.1007/s10732-025-09561-6