Symbolic regression is commonly considered in wide-ranging applications due to its inherent capability for learning both structure and weighting parameters of an interpretable model. However, for ...

The methods for symbolic regression (SR) have come a long way since the days of Koza-style genetic programming (GP). Our goal with this project is to keep a living benchmark of modern symbolic ...

In symbolic regression with formal constraints, the conventional formulation of regression problem is extended with desired properties of the target model, like symmetry, monotonicity, or convexity.