Polyploidism in Deep Neural Networks: m-Parent Evolutionary Synthesis of Deep Neural Networks in Varying Population Sizes

Abstract

Evolutionary deep intelligence was recently proposed to organicallyproduce highly efficient deep neural network architecturesover successive generations. Thus far, current evolutionary synthesisprocesses are based on asexual reproduction, i.e., offspringneural networks are synthesized stochastically from a single parentnetwork. In this study, we investigate the effects of m-parentsexual evolutionary synthesis (m = 1, 2, 3, 5) in combination withvarying population sizes of three, five, and eight synthesized networksper generation. Experimental results were obtained usinga 10% subset of the MNIST handwritten digits dataset, and showthat increasing the number of parent networks results in improvedarchitectural efficiency of the synthesized networks (approximately150x synaptic efficiency and approximately 42–49x cluster efficiency)while resulting in only a 2–3% drop in testing accuracy.