SPINEX Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
MZ Naser, Ahmed Z Naser
cs.LG, stat.CO
Submitted: 2024-11-05
Updated: 2026-08-21
DOI: 10.1007/s11227-025-07132-x
Code: https://github.com/brendenpetersen/deep-symbolic-optimization
License: http://creativecommons.org/licenses/by/4.0/
The gist: This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family.
Terminology
Abstract
This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SPINEX SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics. We conducted extensive benchmarking tests comparing SPINEX SymbolicRegression to over 180 mathematical benchmarking functions from international problem sets that span randomly generated expressions and those based on real physical phenomena. Then, we evaluated the performance of the proposed algorithm in terms of accuracy, expression similarity in terms of presence operators and variables (as compared to the actual expressions), population size, and number of generations at convergence. The results indicate that SPINEX SymbolicRegression consistently performs well and can, in some instances, outperform leading algorithms. In addition, the algorithm's explainability capabilities are highlighted through in-depth experiments.
Sources
- Deep Symbolic Regression for Recurrent Sequences
- Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
- Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
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