Image Classification Using Generative Neuro Evolution for Deep Learning

Abstract

Research into deep learning has demonstrated performance competitive with humans on some visual tasks, however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus neuroevolution for deep learning is investigated in this paper. In particular, the Hypercube-based Neuro Evolution of Augmenting Topologies is a NE approach that can effectively learn large neural structures by training an indirect encoding that compresses the artificial neural network (ANN) weight pattern as a function of geometry. The methodologies are tested on a traditional image classification task as well as one tailored to overhead satellite imagery. The results show that Hyper NEAT struggles with performing image classification by itself, but can be effective in training a feature extractor that other ML approaches can learn from. Thus Neuro Evolution combined with other ML methods provides an intriguing area of research that can replicate the processes in nature.