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.

Roberto Amadini, Maurizio Gabbrielli, Tong Liu, Jacopo Mauro (2023). On the Evaluation of (Meta-)solver Approaches. THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 76, 705-719 [10.1613/jair.1.14102].

On the Evaluation of (Meta-)solver Approaches

Roberto Amadini
;
Maurizio Gabbrielli;Tong Liu;Jacopo Mauro
2023

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.
2023
Roberto Amadini, Maurizio Gabbrielli, Tong Liu, Jacopo Mauro (2023). On the Evaluation of (Meta-)solver Approaches. THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 76, 705-719 [10.1613/jair.1.14102].
Roberto Amadini; Maurizio Gabbrielli; Tong Liu; Jacopo Mauro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/939654
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