Call for Action: towards the next generation of symbolic regression benchmark

TL;DR

This paper updates and expands SRBench, a benchmark for symbolic regression, analyzing diverse algorithms and proposing community-driven standards for evaluation, hyperparameter tuning, and energy efficiency to advance the field.

Contribution

It introduces an improved SRBench with more methods and refined metrics, and calls for community efforts to maintain and evolve the benchmark as a living resource.

Findings

No single algorithm dominates across all datasets.

Trade-offs exist between model complexity, accuracy, and energy consumption.

Standardization can improve comparability and progress in SR.

Abstract

Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this work, we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show that no single algorithm dominates across all datasets. We propose a call for action from SR community in maintaining and evolving SRBench as a living benchmark that reflects the state-of-the-art in symbolic regression, by standardizing hyperparameter tuning, execution constraints, and computational resource allocation. We also propose deprecation criteria to maintain the benchmark's relevance and discuss best practices for improving SR algorithms, such as adaptive hyperparameter tuning and energy-efficient implementations.