Selector: Ensemble-Based Automated Algorithm Configuration

Dimitri Weiß (), Elias Schede () and Kevin Tierney ()
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Dimitri Weiß: Bielefeld University
Elias Schede: Bielefeld University
Kevin Tierney: Bielefeld University

Journal of Heuristics, 2025, vol. 31, issue 3, No 3, 31 pages

Abstract: 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.

Keywords: Algorithm configuration; Ensemble optimization; SAT; MILP; CVRP; TSP; MAX-SAT (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10732-025-09561-6

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