… This article describes simulations on populations of neural networks that both evolve at the
… network's movement) are different tasks. In these conditions, learning influences evolution (…

Although Artificial Neural Networks (ANNs) are important data mining techniques, the search
for the optimal ANN is a challenging task: the ANN should learn the input–output mapping …

… Artificial neural networks can potentially control autonomous … Neuroevolution, ie the artificial
evolution of neural networks, is … tasks may require complex networks with many connections, …

… Therefore we restrict ourselves to the evolution of backpropagation networks. The … evolution
process we compared the best evolved network for each task to a standard network with the …

Neuroevolution, ie evolving artificial neural networks with … advantage from evolving neural
network topologies along with … how it is possible for evolution to both optimize and complexify …

… neural networks. With this in mind, in this work we choose to consider recurrent neural networks
… a unitary hidden to hidden matrix a unitary evolution RNN (uRNN). After experimenting …

… Learning and evolution are two fundamental forms of adaptation. There has been a great
interest in combining learning and evolution with artificial neural networks (ANN’s) in recent …

… applications of neuro-evolution for robot control. Specifically, we are evolving neural networks
to control a robot arm using visual input. Most neural network applications to this problem …

… when we discuss the evolution of architectures and of learning rules later. We distinguish
among three kinds of evolution in EANNs in this article, ie, the evolution of connection weights,…

… DeepNEAT is a most immediate extension of the standard neural network topology-evolution
method NEAT to DNN. It follows the same fundamental process as NEAT: first, a population …