Algorithmic configuration by learning and optimization
Résumé
We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and configurations in order to learn a performance function of the solver. Secondly, we solve a mixed-integer nonlinear program in order to find the best algorithmic configuration based on the performance function.
| Origine | Fichiers produits par l'(les) auteur(s) |
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