  Many experiments have been performed that use evolutionary algorithms for
learning the topology and connection weights of a neural network that controls
a robot or virtual agent. These experiments are not only performed to better
understand basic biological principles, but also with the hope that with
further progress of the methods, they will become competitive for automatically
creating robot behaviors of interest. However, current methods are limited with
respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution
of robot trajectories as an example, we show that by adding four features,
namely (1) freezing of previously evolved structure, (2) temporal scaffolding,
(3) a homogeneous transfer function for output nodes, and (4) mutations that
create new pathways to outputs, to standard methods for the evolution of neural
networks, we can achieve an approximately linear growth of the complexity of
behavior over thousands of generations. Overall, evolved complexity is up to
two orders of magnitude over that achieved by standard methods in the
experiments reported here, with the major limiting factor for further growth
being the available run time. Thus, the set of methods proposed here promises
to be a useful addition to various current neuroevolution methods.
