POVE: A Preoptimized Vault of Expressions for Symbolic Regression Research and Benchmarking

Symbolic Regression (SR) algorithms are powerful machine learning tools that discover mathematical expressions from data, but their evaluation is often hindered by the computational cost of optimizing numerical parameters (also called constants) within candidate expressions. To address this limitation, we introduce POVE (PreOptimized Vault of Expressions), a comprehensive repository of preoptimized symbolic expressions where numerical parameters have been efficiently computed using an established optimization technique in SR (i.e., BFGS) on widely used SR regression problems (i.e., SRBench). In addition to optimized constants, POVE stores the corresponding fitness values, quantifying the accuracy of each expression on its respective regression problem, allowing researchers to directly retrieve both optimized expressions and their precomputed performance metrics from memory (e.g., hash tables or dictionaries). By eliminating the need for computationally expensive numerical optimization steps, POVE enables significantly faster evaluation, large-scale benchmarking, and extensive hyperparameter analysis. It provides a structured and standardized foundation for comparing SR algorithms, assessing their consistency across large numbers of random seeds and investigating the impact of hyperparameters without the overhead of numerical parameter optimization. By offering a collection of precomputed expressions, parameters, and fitness values across diverse regression problems, POVE enhances reproducibility, accelerates experimental workflows, and enables scalable SR research. It serves as a valuable resource for both developing novel SR methodologies and improving existing algorithms by providing a reliable reference for optimal expression discovery. With POVE, SR research can shift away from costly optimization, enabling deeper algorithmic exploration and more comprehensive systematic evaluation. POVE also lowers the barrier to entry for SR research and makes large-scale evaluation significantly more accessible. POVE is publicly available at: https://github.com/kentridgeai/POVE