8 papers
Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression
Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett +3
We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three we…
Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Gabriel Kronberger, Fabricio Olivetti de Franca, Deaglan J. Bartlett +2
Symbolic regression with genetic programming (GPSR) may suffer from overfitting and structural bloat, especially when noise is present. In this paper we evaluate description length…
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…
syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
Lukas Kammerer, Deaglan J. Bartlett, Gabriel Kronberger +2
Baryonic physics has a considerable impact on the distribution of matter in our Universe on scales probed by current and future cosmological surveys, acting as a key systematic in…