  We propose a method for evolving solutions that are robust with respect to
variations of the environmental conditions (i.e. that can operate effectively
in new conditions immediately, without the need to adapt to variations). The
obtained results show how the method proposed is effective and computational
tractable. It permits to improve performance on an extended version of the
double-pole balancing problem, to outperform the best available human-designed
controllers on a car racing problem, and to generate rather effective solutions
for a swarm robotic problem. The comparison of different algorithms indicates
that the CMA-ES and xNES methods, that operate by optimizing a distribution of
parameters, represent the best options for the evolution of robust neural
network controllers.
