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 …

Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …

This article pursues the insight that language models naturally enable an intelligent variation
operator similar in spirit to evolutionary crossover. In particular, language models of …

In recent years, the need for neutral benchmark studies that focus on the comparison of
methods coming from computational sciences has been increasingly recognized by the …

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 …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

Discovering a meaningful symbolic expression that explains experimental data is a
fundamental challenge in many scientific fields. We present a novel, open-source …

In this paper we analyze the effects of using nonlinear least squares for parameter
identification of symbolic regression models and integrate it as local search mechanism in …

Abstract The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based
EA framework that has been shown to perform well in several domains, including Genetic …

Many real-world systems can be naturally described by mathematical formulas. The task of
automatically constructing formulas to fit observed data is called symbolic regression …