We present a comprehensive review of the evolutionary design of neural network
architectures. This work is motivated by the fact that the success of an Artificial Neural …

Deep evolutionary network structured representation (DENSER) is a novel evolutionary
approach for the automatic generation of deep neural networks (DNNs) which combines the …

The term neural network evolution usually refers to network topology evolution leaving the
network's parameters to be trained using conventional algorithms. In this paper we present a …

This paper presents a spiking neuroevolutionary system which implements memristors as
plastic connections, ie, whose weights can vary during a trial. The evolutionary design …

This paper presents dynamic neural-network-based model-predictive control (MPC)
structure for a baker's yeast drying process. Mathematical model consists of two partial …

In the system identification context, neural networks are black-box models, meaning that
both their parameters and structure need to be determined from data. Their identification is …

The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual
crafting of their topology and parameters. Typically, practitioners resort to a time consuming …

Current grammar-based NeuroEvolution approaches have several shortcomings. On the
one hand, they do not allow the generation of Artificial Neural Networks (ANNs) composed …

Neuromorphic computing is a brainlike information processing paradigm that requires
adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this …

Optimizing a neural network's topology is a difficult problem for at least two reasons: the
topology space is discrete, and the quality of any given topology must be assessed by …