6 citations · 18 across the 7 of their papers we have counts for
5 papers · 1 filter
Introduction to Symbolic Regression in the Physical Sciences
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira +1
Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and…
Can Synthetic Data Improve Symbolic Regression Extrapolation Performance?
Fitria Wulandari Ramlan, Colm O'Riordan, Gabriel Kronberger +1
Many machine learning models perform well when making predictions within the training data range, but often struggle when required to extrapolate beyond it. Symbolic regression (SR…
Equality Graph Assisted Symbolic Regression
Fabricio Olivetti de Franca, Gabriel Kronberger
In Symbolic Regression (SR), Genetic Programming (GP) is a popular search algorithm that delivers state-of-the-art results in term of accuracy. Its success relies on the concept of…
rEGGression: an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models
Fabricio Olivetti de Franca, Gabriel Kronberger
Regression analysis is used for prediction and to understand the effect of independent variables on dependent variables. Symbolic regression (SR) automates the search for non-linea…
Improving Genetic Programming for Symbolic Regression with Equality Graphs
Fabricio Olivetti de Franca, Gabriel Kronberger
The search for symbolic regression models with genetic programming (GP) has a tendency of revisiting expressions in their original or equivalent forms. Repeatedly evaluating equiva…