Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical
data. Recently, deep neural models trained on procedurally-generated synthetic datasets …

Production scheduling is an important optimisation problem that reflects the practical and
challenging issues in real-world scheduling applications such as order picking in …

Symbolic regression searches for analytic expressions that accurately describe studied
phenomena. The main promise of this approach is that it may return an interpretable model …

High-stakes applications require AI-generated models to be interpretable. Current
algorithms for the synthesis of potentially interpretable models rely on objectives or …

Genetic programming (GP) is one of the best approaches today to discover symbolic
regression models. To find models that trade off accuracy and complexity, the non …

Symbolic regression searches for analytic expressions that accurately describe studied
phenomena. The main attraction of this approach is that it returns an interpretable model that …

Learning ensembles by bagging can substantially improve the generalization performance
of low-bias, high-variance estimators, including those evolved by Genetic Programming …

Operon is a C++ framework for symbolic regression with the ability to perform local search
by optimizing model coefficients using the Levenberg-Marquardt algorithm. This …

The aim of Symbolic Regression (SR) is to discover interpretable expressions that
accurately describe data. The accuracy of an expression depends on both its structure and …