Evolving robot behaviours with diffusing gas networks

This paper introduces a new type of artificial nervous system and shows that it is possible to use evolutionary computing techniques to find robot controllers based on them. The controllers are built from networks inspired by the modulatory effects of freely diffusing gases, especially nitric oxide, in real neuronal networks. Using Jakobi's radical minimal simulations, successful behaviours have been consistently evolved in far fewer evaluations than were needed when using more conventional connectionist style networks. Indeed the reduction is by a factor of roughly one order of magnitude.