Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …

Binary spatio-temporal data are common in many application areas. Such data can be
considered from many perspectives, including via deterministic or stochastic cellular …

Computational cost in metaheuristics such as Evolutionary Algorithm (EAs) is often a major
concern, particularly with their ability to scale. In data-based training, traditional EAs typically …

Linear scaling has greatly improved the performance of genetic programming when
performing symbolic regression. Linear scaling transforms the output of an expression to …

By the very nature of its representation, symbolic regression through Grammatical Evolution
(GE) stands a chance of being interpretable. However, while GE builds solutions using …

Abstract Fuzzy Pattern Trees evolved using Grammatical Evolution have been shown to be a
robust Explainable Artificial Intelligence technique. We trained a black-box classifier …

Grammar is a key input in grammar-based genetic programming. Grammar design not only
influences performance, but also program size. However, grammar design and the choice of …