Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

• URL: http://arxiv.org/abs/2208.04321v2
• Date: Tue, 18 Apr 2023 14:32:30 GMT
• Title: Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
• Authors: Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, and Kalyanmoy Deb
• Abstract summary: We formulate neural architecture search (NAS) tasks into general multi-objective optimization problems. We analyze the complex characteristics from an optimization point of view. We present an end-to-end pipeline, dubbed $texttEvoXBench$, to generate benchmark test problems for EMO algorithms to run efficiently.
• Score: 30.264524448340406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning have raised higher demands for network architectures considering multiple design criteria: number of parameters/floating-point operations, and inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view; on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view; (ii) we present an end-to-end pipeline, dubbed $\texttt{EvoXBench}$, to generate benchmark test problems for EMO algorithms to run efficiently -- without the requirement of GPUs or Pytorch/Tensorflow; (iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of $\texttt{EvoXBench}$ is available from $\href{https://github.com/EMI-Group/EvoXBench}{\rm{here}}$.

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