Evolvable-Substrate HyperNEAT

The HyperNEAT method exposes the fact that geometrical regularities of the natural world can be adequately represented by artificial neural networks with nodes placed at specific spatial locations. That way, the neuroevolution gains significant benefits and it allows large-scale ANNs to be trained for high dimensional problems, which was impossible with the ordinary NEAT algorithm. At the same time, the HyperNEAT approach is inspired by the structure of a natural brain, which still lacks the plasticity of the natural evolution process. While allowing the evolutionary process to elaborate on a variety of connectivity patterns between network nodes, the HyperNEAT approach exposes a hard limitation on where the network nodes are placed. The experimenter must define the layout of the network nodes from the very beginning, and any incorrect assumption...