# Algorithm Selection on a Meta Level

arXiv: https://arxiv.org/abs/2107.09414 · HTML: https://mlsanity.com/paper/2107.09414/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [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)
- [Extreme Algorithm Selection With Dyadic Feature Representation](https://mlsanity.com/paper/2001.10741/) (2020)
- [Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis](https://mlsanity.com/paper/2007.02816/) (2020)
- [A case study of algorithm selection for the traveling thief problem](https://mlsanity.com/paper/1609.00462/) (2016)
- [Towards Meta-Algorithm Selection](https://mlsanity.com/paper/2011.08784/) (2020)

## Built on by (most cited first, 11 of 11)

- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [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)
- [Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics](https://mlsanity.com/paper/2508.04419/) (2025)
- [Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review](https://mlsanity.com/paper/2607.28631/) (2026)
- [Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection](https://mlsanity.com/paper/2410.07696/) (2024)
- [An Amortized Efficiency Threshold for Comparing Neural and Heuristic Solvers in Combinatorial Optimization](https://mlsanity.com/paper/2605.14624/) (2026)
- [Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection](https://mlsanity.com/paper/2609.13785/) (2026)
- [CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding](https://mlsanity.com/paper/2609.29474/) (2026)

## Abstract (arXiv)

The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.