With the emergence of deep neural networks, many research fields, such as image classification, object detection, speech recognition, natural language processing, machine translation and automatic driving, have made major breakthroughs in technology and the research achievements have been successfully applied in many real-life applications. Combining evolutionary computation and neural architecture search (NAS) is an important approach to improve the performance of deep neural networks. Usually, the related researchers only focus on precision. Thus, the searched neural architectures always perform poorly in the other indexes such as time cost. In this paper, a multi-objective evolutionary algorithm with a probability stack (MOEA-PS) is proposed for NAS, which considers the two objects of precision and time consumption. MOEA-PS uses an adjacency list to represent the internal structure of deep neural networks. Besides, a unique mechanism is introduced into the multi-objective genetic algorithm to guide the process of crossover and mutation when generating offspring. Furthermore, the structure blocks are stacked using a proxy model to generate deep neural networks. The results of the experiments on Cifar-10 and Cifar-100 demonstrate that the proposed algorithm has a similar error rate compared with the most advanced NAS algorithms, but the time cost is lower. Finally, the network structure searched on Cifar-10 is transferred directly to the ImageNet dataset, which can achieve 73.6% classification accuracy.

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study.