In this study we introduce a new technique for symbolic regression that guarantees global optimality. This is achieved by formulating a mixed integer non-linear program (MINLP) whose solution is a symbolic mathematical expression of minimum complexity that explains the observations. We demonstrate our approach by rediscovering Kepler's law on planetary motion using exoplanet data and Galileo's pendulum periodicity equation using experimental data.
@article{arxiv.1710.10720,
title = {Globally Optimal Symbolic Regression},
author = {Vernon Austel and Sanjeeb Dash and Oktay Gunluk and Lior Horesh and Leo Liberti and Giacomo Nannicini and Baruch Schieber},
journal= {arXiv preprint arXiv:1710.10720},
year = {2017}
}
Comments
Presented at NIPS 2017 Symposium on Interpretable Machine Learning