Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters

Abstract

This work presents improvements to a neuroevolution algorithm called Evolutionary eXploration of Augmenting Convolutional Topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). While EXACT has multithreaded and parallel implementations, it has also been implemented as part of a volunteer computing project at the Citizen Science Grid to provide truly large scale computing resources through over 5,500 volunteered computers. Improvements include the development of a new mutation operator, which increased the evolution rate by over an order of magnitude and was also shown to be significantly more reliable in generating new CNNs than the traditional method. Further, EXACT has been extended with a simplex hyperparameter optimization (SHO) method which allows for the co-evolution of hyperparameters, simplifying the task of their selection while generating smaller CNNs with similar predictive ability to those generated with fixed hyperparameters. Lastly, the backpropagation method has been updated with batch normalization and dropout. Compared to previous work, which only achieved prediction rates of 98.32% on the MNIST handwritten digits testing data after 60,000 evolved CNNs, these new advances allowed EXACT to achieve prediction rates of 99.43% within only 12,500 evolved CNNs - rates which are comparable to some of the best human designed CNNs.