On the evaluation of (meta-)solver approaches
Amadini, Roberto, Gabbrielli, Maurizio, Liu, Tong, Mauro, Jacopo
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Feb-17-2022
- Country:
- Asia > China
- Europe
- Denmark > Southern Denmark (0.04)
- France > Occitanie
- Hérault > Montpellier (0.04)
- Italy > Emilia-Romagna
- Metropolitan City of Bologna > Bologna (0.04)
- Genre:
- Overview (0.54)
- Research Report (0.64)
- Technology: