A Classification Surrogate Model based Evolutionary Algorithm for Neural Network Structure Learning

Designing neural networks often requires a large number of artificial intelligence experts. However, such manual processes are time-consuming and require numerous resources. In this paper, we try to search neural network structures automatically for the image classification task. Moreover, considering the huge computational cost of neural architecture search (NAS), we attempt to apply a classification surrogate model based multi-objective evolutionary algorithm to search neural network architectures (CSMEA-Net). The algorithm combines two objectives, i.e., minimizing the validation error and the computational complexity measured by the number of floating-point operations (FLOPs) to achieve Pareto Optimality. In addition, we improve the components of the cell-based search space. The performance of network architectures discovered by our method is evaluated on CIFAR-10 and CIFAR-100 datasets. The experimental results show that the proposed approach can find a higher-performance neural network architecture compared with both hand-crafted as well as automatically-designed networks.