Racing Control Variable Genetic Programming for Symbolic Regression

TL;DR

Racing-CVGP enhances symbolic regression by simultaneously exploring multiple experiment schedules, using a selection process to accelerate discovery of governing equations from data, outperforming previous fixed-schedule methods.

Contribution

The paper introduces Racing-CVGP, a novel approach that dynamically manages multiple experiment schedules to improve the efficiency of symbolic regression.

Findings

Racing-CVGP outperforms CVGP and other symbolic regressors on synthetic datasets.

The method accelerates discovery of physics laws from experimental data.

Early termination of poor schedules saves computational resources.

Abstract

Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing equations from experimental data. Popular approaches based on genetic programming, Monte Carlo tree search, or deep reinforcement learning learn symbolic regression from a fixed dataset. They require massive datasets and long training time especially when learning complex equations involving many variables. Recently, Control Variable Genetic Programming (CVGP) has been introduced which accelerates the regression process by discovering equations from designed control variable experiments. However, the set of experiments is fixed a-priori in CVGP and we observe that sub-optimal selection of experiment schedules delay the discovery process significantly. To overcome this limitation, we propose Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously. A selection scheme similar to that used in selecting good symbolic equations in the genetic programming process is implemented to ensure that promising experiment schedules eventually win over the average ones. The unfavorable schedules are terminated early to save time for the promising ones. We evaluate Racing-CVGP on several synthetic and real-world datasets corresponding to true physics laws. We demonstrate that Racing-CVGP outperforms CVGP and a series of symbolic regressors which discover equations from fixed datasets.