  Modularisation, repetition, and symmetry are structural features shared by
almost all biological neural networks. These features are very unlikely to be
found by the means of structural evolution of artificial neural networks. This
paper introduces NMODE, which is specifically designed to operate on
neuro-modules. NMODE addresses a second problem in the context of evolutionary
robotics, which is incremental evolution of complex behaviours for complex
machines, by offering a way to interface neuro-modules. The scenario in mind is
a complex walking machine, for which a locomotion module is evolved first, that
is then extended by other modules in later stages. We show that NMODE is able
to evolve a locomotion behaviour for a standard six-legged walking machine in
approximately 10 generations and show how it can be used for incremental
evolution of a complex walking machine. The entire source code used in this
paper is publicly available through GitHub.
