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On the Evaluation of (Meta-)solver Approaches

  • Roberto Amadini*
  • , Maurizio Gabbrielli
  • , Tong Liu
  • , Jacopo Mauro
  • *Kontaktforfatter
  • University of Bologna
  • Meituan

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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Abstract

Meta-solver approaches exploit many individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can 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 exposing their strengths and weaknesses.

OriginalsprogEngelsk
TidsskriftJournal of Artificial Intelligence Research
Vol/bind76
Sider (fra-til)705-719
ISSN1076-9757
DOI
StatusUdgivet - 17. mar. 2023

Bibliografisk note

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© 2023 AI Access Foundation. All rights reserved.

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