  We successfully evolved a neural network controller that produces dynamic
walking in a simulated bipedal robot with compliant actuators, a difficult
control problem. The evolutionary evaluation uses a detailed software
simulation of a physical robot. We describe: 1) a novel theoretical method to
encourage populations to evolve "around" local optima, which employs multiple
demes and fitness functions of progressively increasing difficulty, and 2) the
novel genetic representation of the neural controller.
