Artificial neural networks (ANNs) are applied to many real-world problems, ranging from
pattern classification to robot control. In order to design a neural network for a particular task …

Biological neural networks are systems of extraordinary computational capabilities shaped
by evolution, development, and lifelong learning. The interplay of these elements leads to …

Evolutionary Robotics is a method for automatically generating artificial brains and
morphologies of autonomous robots. This approach is useful both for investigating the …

Several researchers have demonstrated how complex action sequences can be learned
through neuroevolution (ie, evolving neural networks with genetic algorithms). However …

The problem of the validity of simulation is particularly relevant for methodologies that use
machine learning techniques to develop control systems for autonomous robots, as, for …

Many complex control problems require sophisticated solutions that are not amenable to
traditional controller design. Not only is it difficult to model real world systems, but often it is …

This article describes simulations on populations of neural networks that both evolve at the
population level and learn at the individual level. Unlike other simulations, the evolutionary …

'Learning to learn'phenomena have been widely investigated in cognition, perception and
more recently also in action. During concept learning tasks, for example, it has been …

The second edition of" Connectionism and the Mind" provides an introduction to
connectionist networks and explores their theoretical and philosophical implications. The …

Artificial neural networks can potentially control autonomous robots, vehicles, factories, or
game players more robustly than traditional approaches. Neuroevolution, ie the artificial …