Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

Solar radiation components assessment is a highly required parameter for solar energy
applications. Due to the non-stationary behavior of solar radiation parameters and variety of …

We present an improved method for symbolic regression that seeks to fit data to formulas
that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

Genetic programming (GP; Koza 1992) is a biologically inspired machine learning method
that evolves computer programs to perform a task. It does this by randomly generating a …

In this study, the predictive power of three different machine learning (ML)-based
approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree) …

This paper presents a new approach for behavioral modeling of structural engineering
systems using a promising variant of genetic programming (GP), namely multi-gene genetic …

In this study, several mathematical, soft computing, and machine learning modeling tools are
used to develop a dependable model for forecasting the compressive strength of cement …

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

Operator learning aims to discover properties of an underlying dynamical system or partial
differential equation (PDE) from data. Here, we present a step-by-step guide to operator …