# Towards Meta-Algorithm Selection

arXiv: https://arxiv.org/abs/2011.08784 · HTML: https://mlsanity.com/paper/2011.08784/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [Machine Learning for Online Algorithm Selection under Censored Feedback](https://mlsanity.com/paper/2109.06234/) (2021)
- [HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection](https://mlsanity.com/paper/2210.17341/) (2022)
- [Automated Machine Learning, Bounded Rationality, and Rational Metareasoning](https://mlsanity.com/paper/2109.04744/) (2021)

## Abstract (arXiv)

Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Over the past years, a plethora of algorithm selectors have been proposed. As an algorithm selector is again an algorithm solving a specific problem, the idea of algorithm selection could also be applied to AS algorithms, leading to a meta-AS approach: Given an instance, the goal is to select an algorithm selector, which is then used to select the actual algorithm for solving the problem instance. We elaborate on consequences of applying AS on a meta-level and identify possible problems. Empirically, we show that meta-algorithm-selection can indeed prove beneficial in some cases. In general, however, successful AS approaches have problems with solving the meta-level problem.