Evolutionary Deep Learning: A Genetic Programming Approach to Image Classification

Benjamin P. Evans, Harith Al-Sahaf, Bing Xue, Mengjie Zhang

Abstract: Image classification is used for many tasks such as recognising handwritten digits, identifying the presence of pedestrians for self-driving cars, and even providing medical diagnosis from cell images. The current state-of-the-art solution for image classification, typically, uses convolutional neural networks (CNNs), however, there are limitations in this approach such as the need for manually crafted architectures and low interpretability. A genetic programming solution is proposed in this paper that aims to overcome these limitations, while also taking advantage of useful operators in CNNs such as convolutions and pooling. The new approach is tested on four widely used benchmark image datasets, and the experimental results show that the new method has achieved comparable performance to the state-of-the-art techniques. Furthermore, the automatically evolved programs are highly interpretable, and visualisations of those programs reveal interesting patterns.