Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Convolutional neural networks (CNNs) have shown outstanding results in different
application tasks. However, the best performance is obtained when customized CNNs …

Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …

Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial
intelligence for the past few years. As the amount of rollout experience data and the size of …

The electric energy production would be much more efficient if accurate estimations of the
future demand were available, since these would allow allocating only the resources …

The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding
(HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While …

This paper introduces a grammar-based general purpose framework for the automatic
search and deployment of potentially Deep Artificial Neural Networks (DANNs). The …

Deep-neuroevolution is the optimisation of deep neural architectures using evolutionary
computation. Amongst these techniques, Fast-Deep Evolutionary Network Structured …

Currently, Big Data techniques and Deep Learning are changing the way humankind
interacts with technology. From content recommendation to technologies capable of creating …