Symbolic-Regression Boosting
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 24 Jun 2022.
Builds on
2 of 24 references.
References · 2
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2018Where are we now? A large benchmark study of recent symbolic regression methodscited by 44
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2023Classy Ensemble: A Novel Ensemble Algorithm for Classificationcited by 1
- 2023A Melting Pot of Evolution and Learningcited by 0
- 2024SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Explorationcited by 0
- 2025Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operatorscited by 0
- 2025Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators5 Oct 2025
- 2024SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration5 Nov 2024
- 2023A Melting Pot of Evolution and Learning8 Jun 2023
- 2023Classy Ensemble: A Novel Ensemble Algorithm for Classification21 Feb 2023