Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/330421 
Erscheinungsjahr: 
2025
Quellenangabe: 
[Journal:] Journal of Heuristics [ISSN:] 1572-9397 [Volume:] 31 [Issue:] 3 [Article No.:] 28 [Publisher:] Springer US [Place:] New York, NY [Year:] 2025
Verlag: 
Springer US, New York, NY
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.
Schlagwörter: 
Algorithm configuration
Ensemble optimization
SAT
MILP
CVRP
TSP
MAX-SAT
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