Symbolic regression by uniform random global search

TL;DR

This paper introduces SRURGS, a simple and robust random search algorithm for symbolic regression, serving as a baseline for comparing more complex methods and aiding in benchmark problem generation.

Contribution

The paper presents SRURGS, a novel pure random search algorithm for symbolic regression, highlighting its robustness and simplicity compared to genetic programming.

Findings

SRURGS is more robust than SRGP on challenging problems.

SRURGS is slower than SRGP on simple problems.

The method is useful for benchmark generation and baseline comparison.

Abstract

Symbolic regression (SR) is a data analysis problem where we search for the mathematical expression that best fits a numerical dataset. It is a global optimization problem. The most popular approach to SR is by genetic programming (SRGP). It is a common paradigm to compare an algorithm's performance to that of random search, but the data comparing SRGP to random search is lacking. We describe a novel algorithm for SR, namely SR by uniform random global search (SRURGS), also known as pure random search. We conduct experiments comparing SRURGS with SRGP using 100 randomly generated equations. Our results suggest that a SRGP is faster than SRURGS in producing equations with good R^2 for simple problems. However, our experiments suggest that SRURGS is more robust than SRGP, able to produce good output in more challenging problems. As SRURGS is arguably the simplest global search algorithm, we believe it should serve as a control algorithm against which other symbolic regression algorithms are compared. SRURGS has only one tuning parameter, and is conceptually very simple, making it a useful tool in solving SR problems. The method produces random equations, which is useful for the generation of symbolic regression benchmark problems. We have released well documented and open-source python code, currently under formal peer-review, so that interested researchers can deploy the tool in practice.