The particle swarm optimization (PSO) algorithm has been the object of many studies and
modifications for more than 25 years. Ranging from small refinements to the incorporation of …

Abstract This paper proposes Drone Squadron Optimization (DSO), a new self-adaptive
metaheuristic for global numerical optimization which is updated online by a hyper-heuristic …

This paper introduces a new version of a hyper-heuristic framework: Generalized Self-
Adapting Particle Swarm Optimization with samples archive (M-GAPSO). This framework is …

Designing an algorithm to solve a given problem is a challenging task due to the variety of
possible design choices and the lack of clear guidelines on how to choose and/or combine …

Abstract Selecting and optimizing Convolutional Neural Networks (CNNs) has become a
very complex task given the number of associated optimizable parameters, as well as the …

The use of Convolutional Neural Networks (CNNs) has been demonstrated to be a solid
approach for solving many machine learning problems, such as image classification and …

Abstract Particle Swarm Optimization (PSO) is largely used to solve optimization problems
effectively. Nonetheless, the PSO performance depends on the fine tuning of different …

Researches point out to the importance of automatic design of multi-objective evolutionary
algorithms. Because in general, algorithms automatically designed outperform traditional …

Image segmentation is a relevant problem in computer vision present in multiple application
domains. One of the most used methods for image segmentation is U-net, a type of …

The authors present a thinly veiled attack on the popular Grammatical Evolution (GE)
system, the second in the space of year. The paper presents itself as a philosophical …