  Evolutionary deep intelligence was recently proposed as a method for
achieving highly efficient deep neural network architectures over successive
generations. Drawing inspiration from nature, we propose the incorporation of
sexual evolutionary synthesis. Rather than the current asexual synthesis of
networks, we aim to produce more compact feature representations by
synthesizing more diverse and generalizable offspring networks in subsequent
generations via the combination of two parent networks. Experimental results
were obtained using the MNIST and CIFAR-10 datasets, and showed improved
architectural efficiency and comparable testing accuracy relative to the
baseline asexual evolutionary neural networks. In particular, the network
synthesized via sexual evolutionary synthesis for MNIST had approximately
double the architectural efficiency (cluster efficiency of 34.29X and synaptic
efficiency of 258.37X) in comparison to the network synthesized via asexual
evolutionary synthesis, with both networks achieving a testing accuracy of
~97%.
