On the Evaluation of (Meta-)solver Approaches

Roberto Amadini, Maurizio Gabbrielli, Tong Liu, Jacopo Mauro

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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.

TidsskriftJournal of Artificial Intelligence Research
Sider (fra-til)705-719
StatusUdgivet - 17. mar. 2023

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