Racing Control Variable Genetic Programming for Symbolic Regression
Nan Jiang Yexiang Xue
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.