21 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…
The Degeneracy Distillery
T. Lucas Makinen, Deaglan J. Bartlett, Niall Jeffrey +1
When two or more parameters or labels produce similar data, they are degenerate, or hard to distinguish. Degeneracies render both label prediction and inverse problems difficult, s…
Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
Laura Sommovigo, Deaglan J. Bartlett, Rachel K. Cochrane +3
Dust attenuation is a major source of systematic uncertainty in both SED fitting and forward modeling of galaxy populations, yet the functional form used to parameterize attenuatio…
CMBolic: Symbolic emulators for the Cosmic Microwave Background. I. Lensing
David M. J. Vokrouhlicky, Constantinos Skordis, Deaglan J. Bartlett +2
We present the first installment of CMBolic: a suite of symbolic cosmic microwave background (CMB) emulators. In this instance, we emulate the CMB lensing potential power spectrum…
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…
Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
Andrei Constantin, Deaglan Bartlett, Harry Desmond +1
Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena, physical equations exhibit structural…