Evolutionary convolutional neural network for image classification based on multi-objective genetic programming with leader–follower mechanism

Abstract

AbstractAs a popular research in the field of artificial intelligence in the last 2 years, evolutionary neural architecture search (ENAS) compensates the disadvantage that the construction of convolutional neural network (CNN) relies heavily on the prior knowledge of designers. Since its inception, a great deal of researches have been devoted to improving its associated theories, giving rise to many related algorithms with pretty good results. Considering that there are still some limitations in the existing algorithms, such as the fixed depth or width of the network, the pursuit of accuracy at the expense of computational resources, and the tendency to fall into local optimization. In this article, a multi-objective genetic programming algorithm with a leader–follower evolution mechanism (LF-MOGP) is proposed, where a flexible encoding strategy with variable length and width based on Cartesian genetic programming is designed to represent the topology of CNNs. Furthermore, the leader–follower evolution mechanism is proposed to guide the evolution of the algorithm, with the external archive set composed of non-dominated solutions acting as the leader and an elite population updated followed by the external archive acting as the follower. Which increases the speed of population convergence, guarantees the diversity of individuals, and greatly reduces the computational resources. The proposed LF-MOGP algorithm is evaluated on eight widely used image classification tasks and a real industrial task. Experimental results show that the proposed LF-MOGP is comparative with or even superior to 35 existing algorithms (including some state-of-the-art algorithms) in terms of classification error and number of parameters.