A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming

TL;DR

This paper compares recent neural network-based symbolic regression algorithms with traditional genetic programming methods, finding that traditional methods still outperform newer approaches on novel datasets.

Contribution

It provides a systematic comparison of emerging neural symbolic regression systems against established genetic programming techniques using new datasets.

Findings

Traditional GP methods outperform recent neural approaches.

New datasets reveal limitations of recent neural symbolic regression.

Neural methods have yet to surpass classical genetic programming in this domain.

Abstract

Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and map data in a way that can be understood by scientists. Recent advancements, have attempted to bridge the gap between these two fields; new methodologies attempt to fuse the mapping power of neural networks and deep learning techniques with the explanatory power of symbolic regression. In this paper, we examine these new emerging systems and test the performance of an end-to-end transformer model for symbolic regression versus the reigning traditional methods based on genetic programming that have spearheaded symbolic regression throughout the years. We compare these systems on novel datasets to avoid bias to older methods who were improved on well-known benchmark datasets. Our results show that traditional GP methods as implemented e.g., by Operon still remain superior to two recently published symbolic regression methods.