Evolving fixed-weight networks for learning robots

Research in the field of evolutionary robotics has begun to investigate the evolution of learning controllers for autonomous robots. Research in this area has achieved promising results, but research to date has focussed on the evolution of neural networks incorporating synaptic plasticity. There has been little investigation of possible alternatives, although the importance of exploring such alternatives is recognised. This paper describes a first step towards addressing this issue. Using networks with fixed synaptic weights and 'leaky integrator' neurons, we evolve robot controllers capable of learning and thus exploiting regularities occurring within their environment.