Genetic programming (GP) is a well-known evolutionary computation technique, which has
been successfully used to solve various problems, such as optimization, image analysis …

Evolutionary computation techniques have had limited capabilities in solving large-scale
problems due to the large search space demanding large memory and much longer training …

In this paper, a new genetic programming (GP) algorithm for symbolic regression problems
is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical …

In this review article, we provide a comprehensive guide to the endeavor of problem
decomposition within the field of Genetic Programming (GP), specifically tree-based GP for …

Despite the empirical success of Genetic programming (GP) in various symbolic regression
applications, GP is not still known as a reliable problem-solving technique in this domain …

Modern genetic programming (GP) operates within the statistical machine learning (SML)
framework. In this framework, evolution needs to balance between approximation of an …

The scale at which the human race consumes data has increased exponentially in recent
years. One key part in this increase has been the usage of smart phones and connected …

This paper introduces a new method that improves the generalization ability of genetic
programming (GP) for symbolic regression problems, named variance-based layered …

Genetic programming (GP) is a well established evolutionary computation technique that
automatically generates a computer program to solve a given problem. GP has been …

The purpose of this paper is to improve the efficiency of Genetic Programming (GP) by
decomposing a regression problem into several subproblems. An optimization problem is …