# HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection

arXiv: https://arxiv.org/abs/2210.17341 · HTML: https://mlsanity.com/paper/2210.17341/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [ASlib: A Benchmark Library for Algorithm Selection](https://mlsanity.com/paper/1506.02465/) (2015)
- [Extreme Algorithm Selection With Dyadic Feature Representation](https://mlsanity.com/paper/2001.10741/) (2020)
- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis](https://mlsanity.com/paper/2007.02816/) (2020)
- [Towards Meta-Algorithm Selection](https://mlsanity.com/paper/2011.08784/) (2020)

## Built on by (most cited first, 3 of 3)

- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection](https://mlsanity.com/paper/2405.11349/) (2024)

## Abstract (arXiv)

It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic problem, which is the most suitable algorithm to solve it? As such, the AS problem has received considerable attention resulting in various approaches - many of which either solve a regression or ranking problem under the hood. Although both of these formulations yield very natural ways to tackle AS, they have considerable weaknesses. On the one hand, correctly predicting the performance of an algorithm on an instance is a sufficient, but not a necessary condition to produce a correct ranking over algorithms and in particular ranking the best algorithm first. On the other hand, classical ranking approaches often do not account for concrete performance values available in the training data, but only leverage rankings composed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon foreSts - a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on splits optimized on a hybrid ranking and regression loss function. As our preliminary experimental study on ASLib shows, HARRIS improves over standard algorithm selection approaches on some scenarios showing that combining ranking and regression in trees is indeed promising for AS.