A Memetic Algorithm for Evolving Deep Convolutional Neural Network in Image Classification

Abstract

As evolutionary algorithms (EAs) are robust to the problem formulation and easy to use, there is a growing interest in designing EAs for automated neural architecture search in recent years. In particular, EvoCNN is a recently proposed evolutionary algorithm to automate the configuration of a deep Convolutional Neural Network (CNN) for image classification. Its efficacy has been confirmed against 22 existing algorithms for CNN configuration, on the widely used image classification tasks. However, despite the success enjoyed by this method, we note that there are several limitations existed in this method. For example, only chain structured network is considered for evolution. Further, there are many decision variables, which is computational expensive. In this paper, we embark a study on evolutionary neural architecture search by proposing a memetic algorithm (MA), with the aim of addressing the problems mentioned above. Particularly, first of all, besides evolving the chain structured network, local search is designed for multibranch network search. Next, to reduce the network parameters for optimization, we focus on the architecture search only on the convolutional layers. Moreover, based on a recent hypothesis in the literature, the network evaluation is conducted based on only the early training process in our proposed MA. To confirm the efficacy of the proposed method, comprehensive empirical studies are conducted against EvoCNN for NAS, on the commonly used image classification benchmarks.