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<p>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 <span>Selector</span>, 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.</p>

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Selector: Ensemble-Based Automated Algorithm Configuration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月26日

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  • Dimitri Weiß,
  • Elias Schede,
  • Kevin Tierney

摘要

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.