On the evaluation of (meta-)solver approaches
Artificial Intelligence
2023-03-21 v1 Machine Learning
Performance
Abstract
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.
Keywords
Cite
@article{arxiv.2202.08613,
title = {On the evaluation of (meta-)solver approaches},
author = {Roberto Amadini and Maurizio Gabbrielli and Tong Liu and Jacopo Mauro},
journal= {arXiv preprint arXiv:2202.08613},
year = {2023}
}