guidelines. In the first set, controllers were evolved that allowed a Khepera robot to perform a simple memory task in the real world. In the second set, controllers were evolved for the Sussex University gantry robot that were able to distinguish visually a triangle from a square, under extremely noisy real-world conditions, and to steer the robot toward the triangle. In both cases, controllers that were reliably fit in simulation displayed extremely robust behavior when downloaded into reality.

This paper introduces a new type of artificial neural network (GasNets) 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. Evolutionary robotics techniques were used to develop control networks and visual morphologies to enable a robot to achieve a target discrimination task under very noisy lighting conditions. A series of evolutionary runs with and without the gas modulation active demonstrated that networks incorporating modulation by diffusing gases evolved to produce successful controllers considerably faster than networks without this mechanism. GasNets also consistently achieved evolutionary success in far fewer evaluations than were needed when using more conventional connectionist style networks.2

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