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 ...

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.