ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization

TL;DR

ParFam introduces a continuous optimization-based approach to symbolic regression using parametric function families, enhanced by a transformer-guided extension, achieving state-of-the-art results efficiently.

Contribution

This paper presents ParFam, a novel continuous optimization method for symbolic regression, and extends it with DL-ParFam, a transformer-guided acceleration technique, simplifying and improving over existing methods.

Findings

Achieves state-of-the-art results on SRBench benchmark.

Transformer guidance accelerates optimization by up to two orders of magnitude.

Theoretically analyzes the expressivity of ParFam.

Abstract

The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of financial markets from given data. Various methods exist to address the problem of SR, often based on genetic programming. However, these methods are usually complicated and involve various hyperparameters. In this paper, we present our new approach ParFam that utilizes parametric families of suitable symbolic functions to translate the discrete symbolic regression problem into a continuous one, resulting in a more straightforward setup compared to current state-of-the-art methods. In combination with a global optimizer, this approach results in a highly effective method to tackle the problem of SR. We theoretically analyze the expressivity of ParFam and demonstrate its performance with extensive numerical experiments based on the common SR benchmark suit SRBench, showing that we achieve state-of-the-art results. Moreover, we present an extension incorporating a pre-trained transformer network DL-ParFam to guide ParFam, accelerating the optimization process by up to two magnitudes. Our code and results can be found at https://github.com/Philipp238/parfam.