DES-HyperNEAT: Towards Multiple Substrate Deep ANNs

Neuroevolution (NE), inspired by the natural evolution of biological brains, may be applied to the evolution of both weights and the topology of Artificial Neural Networks (ANNs). DES-HyperNEAT, proposed herein, further extends existing NE algorithms to enable evolvable substrate topologies with non-fixed node positions - a desirable feature for optimisation of deep ANNs. DES-HyperNEAT builds on HyperNEAT and ES-HyperNEAT whilst adding beneficial features from NEAT and MSS-HyperNEAT. The preliminary experiments in this work consider potential variants of DES-HyperNEAT, evaluating such variants in terms of performance and efficiency on the Iris, Wine and Retina datasets. Further, DES-HyperNEAT, ES-HyperNEAT, HyperNEAT and NEAT are compared to highlight whether the DES-HyperNEAT extension has merit in terms of performance and efficiency.