# On the evaluation of (meta-)solver approaches

arXiv: https://arxiv.org/abs/2202.08613 · HTML: https://mlsanity.com/paper/2202.08613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [ASlib: A Benchmark Library for Algorithm Selection](https://mlsanity.com/paper/1506.02465/) (2015)
- [The Algorithm Selection Competitions 2015 and 2017](https://mlsanity.com/paper/1805.01214/) (2018)
- [sunny-as2: Enhancing SUNNY for Algorithm Selection](https://mlsanity.com/paper/2009.03107/) (2020)
- [ICON Challenge on Algorithm Selection](https://mlsanity.com/paper/1511.04326/) (2015)

## Abstract (arXiv)

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.