As 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.

作为近两年人工智能领域的热门研究，进化神经架构搜索（ENAS）弥补了卷积神经网络（CNN）的构建严重依赖设计者先验知识的缺点。自提出以来，大量的研究致力于完善其相关理论，产生了许多相关算法，并取得了很好的效果。考虑到现有算法仍存在一些局限性，如网络深度或宽度固定，以牺牲计算资源为代价追求精度，容易陷入局部优化等。在本文中，提出了一种具有领导者-跟随者进化机制的多目标遗传规划算法（LF-MOGP），其中基于笛卡尔遗传规划的可变长度和宽度的灵活编码策略被设计用来表示 CNN 的拓扑结构。此外，提出了领导者-跟随者进化机制来指导算法的进化，由非支配解组成的外部档案集作为领导者，精英种群更新，外部档案作为跟随者。从而提高了种群收敛速度，保证了个体的多样性，大大减少了计算资源。所提出的 LF-MOGP 算法在八个广泛使用的图像分类任务和一个真实的工业任务上进行了评估。