Evolving conductive polymer neural networks on wetware

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Evolving Neural Networks on GPUs

Financial Time Series prediction attempts to model the behavior of financial markets using, among other things, tools like technical, intermarket, and fundamental indicators. Accurate prediction, however, is difficult for a number of reasons: financial markets are influenced, often in a non-linear, sometimes time-lagged fashion, by factors including interest and exchange rates, the rate of econ...

Evolving Deep Neural Networks

Œe success of deep learning depends on €nding an architecture to €t the task. As deep learning has scaled up to more challenging tasks, the architectures have become dicult to design by hand. Œis paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters,...

Evolving Complex Neural Networks

Complex networks like the scale-free model proposed by BarabasiAlbert are observed in many biological systems and the application of this topology to artificial neural network leads to interesting considerations. In this paper, we present a preliminary study on how to evolve neural networks with complex topologies. This approach is utilized in the problem of modeling a chemical process with the...

Evolving Artificial Neural Networks

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 years. This paper: 1) reviews different combinations between ANN’s and evolutionary algorithms (EA’s), including using EA’s to evolve ANN connection weights, architectures, learning rules, and input features; 2) dis...