Abstract. We propose and evaluate a novel approach to the online syn-thesis of neural controllers for autonomous robots. We combine online evolution of weights and network topology with neuromodulated learn-ing. We demonstrate our method through a series of simulation-based ex-periments in which an e-puck-like robot must perform a dynamic concur-rent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. Our results show that when neuromodulated learning is employed, neural controllers are synthesised faster than by evolution alone. We demonstrate that the online evolu-tionary process is capable of generating controllers well adapted to the periodic task changes. An analysis of the evolved networks shows that they are characterised by specialised modulatory neurons that exclusively regulate the output neurons
