Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches

Abstract

Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the design of the CNN architectures is mainly done manually, which is very time consuming and requires expertise in both problem domains and CNNs. In this chapter, we will describe an approach to the use of particle swarm optimization (PSO) for automatically searching for and learning the optimal CNN architectures. We will provide an encoding strategy inspired by computer networks to encode CNN layers and to allow the proposed method to learn variable-length CNN architectures by focusing only on the single objective of maximizing the classification accuracy. A surrogate dataset will be used to speed up the evolutionary learning process. We will also include a multi-objective way for PSO to evolve CNN architectures in the chapter. The PSO-based algorithms are examined and compared with state-of-the-art algorithms on a number of widely used image classification benchmark datasets. The experimental results show that the proposed algorithms are strong competitors to the state-of-the-art algorithms in terms of classification error. A major advantage of the proposed methods is the automated design of CNN architectures without requiring human intervention and good performance of the learned CNNs.