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On the evaluation of (meta-)solver approaches

arXiv 2202.08613 ↗First posted 17 Feb 2022cs.AI cs.LG cs.PFcited by 0

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Abstract (arXiv)

Meta-solver approaches exploits a number of individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can simply adopt the metrics typically used for individual solvers (e.g., runtime or solution quality), or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers, by underlying their strengths and weaknesses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 17 Feb 2022.

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4 of 15 references.

References · 4

  1. 2015ASlib: A Benchmark Library for Algorithm Selectioncited by 37
  2. 2018The Algorithm Selection Competitions 2015 and 2017cited by 12
  3. 2020sunny-as2: Enhancing SUNNY for Algorithm Selectioncited by 5
  4. 2015ICON Challenge on Algorithm Selectioncited by 3

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