This paper explains in detail a biologically inspired encoding scheme for the artificial evolution of neural network robot controllers. Under the scheme, an individual cell divides and moves, in response to protein interactions with an artificial genome, to form a multi-cellular 'organism'. After differentiation dendrites grow out of each cell, guided by chemically sensitive growth cones, to form connections between the cells. The resultant network is then interpreted as a recurrent neural network robot controller. Results are given of preliminary experiments to evolve robot controllers for both corridor following and object avoidance tasks.
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