Symbolic regression by uniform random global search

Abstract

This work describes a novel algorithm for symbolic regression, namely symbolic regression by uniform random global search (SRURGS). SRURGS has only one tuning parameter and is very simple conceptually. 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 which passed a formal peer-review, so that interested researchers can deploy the tool in practice. We conduct experiments comparing SRURGS with symbolic regression by genetic programming (SRGP) using 100 randomly generated equations. Our results suggest that 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.