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

Coevolution (ie, the evolution of two or more competing populations with coupled fitness)
has several features that may potentially enhance the power of adaptation of artificial …

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

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary
robotics. It has been suggested that carefully validated simulations with a proper treatment of …

Biological life is in control of its own means of reproduction, which generally involves
complex, autocatalysing chemical reactions. But this autonomy of design and manufacture …

Dynamical neural networks are being increasingly employed in a variety of contexts,
including as simple model nervous systems for autonomous agents. For this reason, there is …

Evolutionary robotics (ER) aims at automatically designing robots or controllers of robots
without having to describe their inner workings. To reach this goal, ER researchers primarily …

In most modern video games, character behavior is scripted; no matter how many times the
player exploits a weakness, that weakness is never repaired. Yet, if game characters could …

For several years now, various researchers have endeavored to apply artificial evolution to
the automatic design of control systems for real robots. One of the major challenges they …

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 …