Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

Are continuing changes in the Arctic influencing wind patterns and the occurrence of
extreme weather events in northern mid-latitudes? The chaotic nature of atmospheric …

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 …

The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …

We propose a new means of executing a genetic program which improves its output quality.
Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and …

In this paper the application of machine learning techniques for the development of
constitutive material models is being investigated. A flow stress model, for strain rates …

We introduce a framework to discover interpretable regression models for high-stakes
decision making in the context of safety-critical systems. The core of our proposal is a multi …

In the implementation of traditional GP algorithm as models are evolved in a single deme
(an environment in which a population of models is evolved) it may tend to produce sub …