Climate neutrality is one of the greatest challenges of our century, and a decarbonised
energy system is a key step towards this goal. To this end, the electricity system is expected …

We investigate the addition of constraints on the function image and its derivatives for the
incorporation of prior knowledge in symbolic regression. The approach is called shape …

Interaction–Transformation (IT) is a new representation for Symbolic Regression that
reduces the space of solutions to a set of expressions that follow a specific structure. The …

Despite the empirical success of Genetic programming (GP) in various symbolic regression
applications, GP is not still known as a reliable problem-solving technique in this domain …

Many risk-sensitive applications require Machine Learning (ML) models to be interpretable.
Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyper …

Symbolic regression (SR) is an important problem with many applications, such as
automatic programming tasks and data mining. Genetic programming (GP) is a commonly …

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

Semantic GP is a promising branch of GP that introduces semantic awareness during
genetic evolution to improve various aspects of GP. This paper presents a new Semantic GP …

In the multi-class classification problem GP plays an important role when combined with
other non-GP classifiers. However, when GP performs the actual classification (without …

Learning features from raw data is an important topic in machine learning. This paper
presents Genetic Program Feature Learner (GPFL), a novel generative GP feature learner …