A first step toward incremental evolution of convolutional neural networks

Dustin K. Barnes, Sara R. Davis, Emily M. Hand, Sushil J. Louis

Abstract: We introduce a novel algorithm - ConvNEAT - that evolves a convolutional neural network (CNN) from a minimal architecture. Convolutional and dense nodes are evolved without restriction to the number of nodes or connections between nodes. The proposed work advances the field with ConvNEAT's ability to evolve arbitrary minimal architectures with multi-dimensional inputs using GPU processing.