Recently, the expert-crafted neural architectures is increasing overtaken by the utilization of neural architecture search (NAS) and automatic generation (and tuning) of network structures which has a close relation to the Hyperparameter Optimization and Auto Machine Learning (AutoML). After the earlier NAS attempts to optimize only the prediction accuracy, Multi-Objective Neural architecture Search (MONAS) has been attracting attentions which considers more goals such as computational complexity, power consumption, and size of the network for optimization, reaching a trade-off between the accuracy and other features like the computational cost. In this paper, we present an overview of principal and state-of-the-art works in the field of MONAS. Starting from a well-categorized taxonomy and formulation for the NAS, we address and correct some miscategorizations in previous surveys of the NAS field. We also provide a list of all known objectives used and add a number of new ones and elaborate their specifications. We have provides analyses about the most important objectives and shown that the stochastic properties of some the them should be differed from deterministic ones in the multi-objective optimization procedure of NAS. We finalize this paper with a number of future directions and topics in the field of MONAS.

翻译：近年来，专家手工设计的神经架构正逐渐被神经架构搜索（NAS）以及网络结构的自动生成与调优所取代，这与超参数优化和自动机器学习（AutoML）密切相关。在早期NAS仅专注于优化预测准确率之后，多目标神经架构搜索（MONAS）逐渐引起关注，它在优化过程中考虑更多目标，如计算复杂度、功耗和网络规模，从而在准确率与计算成本等其他特征之间达成平衡。本文概述了MONAS领域中基础性和前沿性的研究工作。我们从NAS的合理分类和形式化定义出发，指出并纠正了先前NAS综述中的一些分类错误。我们列出了所有已知的优化目标，补充了若干新目标，并详细阐述了它们的特性。我们对最重要的目标进行了分析，并指出在NAS的多目标优化过程中，部分目标的随机性质应与确定性目标加以区分。最后，我们探讨了MONAS领域的若干未来研究方向与主题。