299 citations · 2.2k across the 101 of their papers we have counts for
3 papers · 1 filter
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…
The Inefficiency of Genetic Programming for Symbolic Regression
Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond +2
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This…