Spiking Neural Networks (SNNs) have attracted recent interest due to the...
Maintaining genetic diversity as a means to avoid premature convergence ...
This work proposes Adaptive Facilitated Mutation, a self-adaptive mutati...
Grammar-Guided Genetic Programming (GGGP) employs a variety of insights ...
NeuroEvolution automates the generation of Artificial Neural Networks th...
In the context of generative models, text-to-image generation achieved
i...
The grammars used in grammar-based Genetic Programming (GP) methods have...
This work proposes an extension to Structured Grammatical Evolution (SGE...
Neuroevolutionary algorithms, automatic searches of neural network struc...
Artificial Neural Networks (ANNs) became popular due to their successful...
Grammatical Evolution (GE) is one of the most popular Genetic Programmin...
Generative Adversarial Networks (GANs) are powerful generative models th...
Generative adversarial networks (GANs) achieved relevant advances in the...
The choice of a proper learning rate is paramount for good Artificial Ne...
Generative Adversarial Networks (GANs) are an adversarial model that ach...
The deployment of Machine Learning (ML) models is a difficult and
time-c...
NeuroEvolution (NE) methods are known for applying Evolutionary Computat...
Generative adversarial networks (GAN) present state-of-the-art results i...
Generative adversarial networks (GAN) became a hot topic, presenting
imp...
Machine learning models are widely adopted in scenarios that directly af...
The goal of this work is to investigate the possibility of improving cur...
This paper proposes a new extension to Deep Evolutionary Network Structu...
Deep Evolutionary Network Structured Representation (DENSER) is a novel
...
Current grammar-based NeuroEvolution approaches have several shortcoming...