The Utilities of Evolutionary Multiobjective Optimization for Neural Architecture Search – An Empirical Perspective
Evolutionary algorithms have been widely used in neural architecture search (NAS) in recent years due to their flexible frameworks and promising performance. However, we noticed a lack of attention to algorithm selection, and single-objective algorithms were preferred despite the multiobjective nature of NAS, among prior arts. To explore the reasons behind this preference, we tested mainstream evolutionary algorithms on several standard NAS benchmarks, comparing single and multi-objective algorithms. Additionally, we validated whether the latest evolutionary multi-objective optimization (EMO) algorithms lead to improvement in NAS problems compared to classical EMO algorithms. Our experimental results provide empirical answers to these questions and guidance for the future development of evolutionary NAS algorithms.
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|Document Type||Conference paper|
Southern University of Science and Technology,Shenzhen,China
|First Author Affilication||Southern University of Science and Technology|
|Corresponding Author Affilication||Southern University of Science and Technology|
|First Author's First Affilication||Southern University of Science and Technology|
Liu，Xukun. The Utilities of Evolutionary Multiobjective Optimization for Neural Architecture Search – An Empirical Perspective[C],2023:179-195.
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