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 …

The authors showcase the potential of symbolic regression as an analytic method for use in
materials research. First, the authors briefly describe the current state-of-the-art method …

In this paper we provide a broad benchmarking of recent genetic programming approaches
to symbolic regression in the context of state of the art machine learning approaches. We …

Machine learning (ML) has emerged as an invaluable approach for deriving predictive
models in the catalysis field. While they are successful in making accurate predictions, many …

Energy consumption has long been emphasized as an important policy issue in today's
economies. In particular, the energy efficiency of residential buildings is considered a top …

The growing number of e-commerce orders is leading to increased risk management to
prevent default in payment. Default in payment is the failure of a customer to settle a bill …

Forest fires importantly influence our environment and lives. The ability of accurately
predicting the area that may be involved in a forest fire event may help in optimizing fire …

Allowing the population size to variate during the evolution can bring advantages to
evolutionary algorithms (EAs), retaining computational effort during the evolution process …

In recent years, genetic programming has achieved impressive results on evolutionary
feature construction tasks. To increase search effectiveness, researchers have developed …

Geometric semantic genetic programming (GSGP) is a well-known variant of genetic
programming (GP) where recombination and mutation operators have a clear semantic …