# Symbolic-Regression Boosting

arXiv: https://arxiv.org/abs/2206.12082 · HTML: https://mlsanity.com/paper/2206.12082/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Where are we now? A large benchmark study of recent symbolic regression methods](https://mlsanity.com/paper/1804.09331/) (2018)

## Built on by (most cited first, 4 of 4)

- [Classy Ensemble: A Novel Ensemble Algorithm for Classification](https://mlsanity.com/paper/2302.10580/) (2023)
- [A Melting Pot of Evolution and Learning](https://mlsanity.com/paper/2306.04971/) (2023)
- [SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration](https://mlsanity.com/paper/2411.03358/) (2024)
- [Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators](https://mlsanity.com/paper/2510.05178/) (2025)

## Abstract (arXiv)

Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.