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 last few years have witnessed a surge in methods for symbolic regression, from
advances in traditional evolutionary approaches to novel deep learning-based systems …

When learning from high-dimensional data for symbolic regression (SR), genetic
programming (GP) typically could not generalize well. Feature selection, as a data …

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 …

Semantic Backpropagation (SB) is a recent technique that promotes effective variation in
tree-based genetic programming. The basic idea of SB is to provide information on what …

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 recent years, genetic programming has achieved impressive results on evolutionary
feature construction tasks. To increase search effectiveness, researchers have developed …

Geometric semantic genetic programming (GP) has recently attracted much attention. The
key innovations are inducing a unimodal fitness landscape in the semantic space and …

Behavioral Program Synthesis with Genetic Programming Page 1 Studies in Computational
Intelligence 618 Krzysztof Krawiec Behavioral Program Synthesis with Genetic Programming …

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 …