New Publication by CBEE Faculty

Dr. Tyler R Josephson, Assistant Professor, Chemical, Biochemical, and Environmental Engineering department at the University of Maryland, Baltimore County, along with UMBC student and alumni, Charles Fox, Neil D Tran, F Nikki Nacion, and Samiha Sharlin, have a new publication in Machine Learning: Science and Technology titled "Incorporating background knowledge in symbolic regression using a computer algebra system."

The article is available here:
Incorporating background knowledge in symbolic regression using a computer algebra system - IOPscience

DOI 10.1088/2632-2153/ad4a1e

Abstract:

Symbolic regression (SR) can generate interpretable, concise expressions that fit a given dataset, allowing for more human understanding of the structure than black-box approaches. The addition of background knowledge (in the form of symbolic mathematical constraints) allows for the generation of expressions that are meaningful with respect to theory while also being consistent with data. We specifically examine the addition of constraints to traditional genetic algorithm (GA) based SR (PySR) as well as a Markov-chain Monte Carlo (MCMC) based Bayesian SR architecture (Bayesian Machine Scientist), and apply these to rediscovering adsorption equations from experimental, historical datasets. We find that, while hard constraints prevent GA and MCMC SR from searching, soft constraints can lead to improved performance both in terms of search effectiveness and model meaningfulness, with computational costs increasing by about an order of magnitude. If the constraints do not correlate well with the dataset or expected models, they can hinder the search of expressions. We find incorporating these constraints in Bayesian SR (as the Bayesian prior) is better than by modifying the fitness function in the GA.