Several contemporaneous image processing and computer vision systems rely upon the full-
reference image quality assessment (IQA) measures. The single-scale structural similarity …

Allowing the population size to variate during the evolution can bring advantages to
evolutionary algorithms (EAs), retaining computational effort during the evolution process …

Geometric semantic genetic programming (GSGP) and linear scaling (LS) have both,
independently, shown the ability to outperform standard genetic programming (GP) for …

Geometric semantic genetic programming (GSGP) represents one of the most promising
developments in the area of evolutionary computation (EC) in the last decade. The results …

The growing production of digital content and its dissemination across the worldwide web
require eficient and precise management. In this context, image quality assessment …

Abstract Geometric Semantic Genetic Programming (GSGP) has shown notable success in
symbolic regression with the introduction of Linear Scaling (LS). This achievement stems …

In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-
based GP (TGP), and then analyze their behavior using five real-world datasets. We show …

Sharpness-Aware Minimization (SAM) was recently introduced as a regularization
procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) …

We present a unified deep learning framework for the recognition of user identity and the
recognition of imagined actions, based on electroencephalography (EEG) signals, for …

Metaheuristic optimization algorithms are increasingly used to reach near-optimal solutions
for complex and large-scale problems that cannot be solved in due time by exact methods …