A New Deterministic Technique for Symbolic Regression

TL;DR

This paper introduces a deterministic method for symbolic regression that efficiently finds mathematical expressions fitting data, offering comparable accuracy to machine learning methods with lower computational cost and enhanced interpretability.

Contribution

The paper presents a novel deterministic approach to symbolic regression that avoids population-based methods, providing faster results and more interpretable expressions.

Findings

Achieves results comparable to other machine learning methods.

Operates with significantly lower computational time.

Produces simpler, user-friendly mathematical expressions.

Abstract

This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is deterministic, and does not involve the creation of a population of initial solutions. Instead of it, a simple expression is being grown until it fits the data. The experiments performed show that the results are as good as other Machine Learning methods, in a very low computational time. Another advantage of this technique is that the complexity of the expressions can be limited, so the system can return mathematical expressions that can be easily analysed by the user, in opposition to other techniques like GSGP.