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In addition, a new environmental selection based on a reference population is designed to keep diversity of the population in each generation and an infill criterion for handling the trade-off between convergence and model uncertainty is proposed for re-evaluation. Experimental results on three different NASBench and DARTS search space illustrate that network embedding makes the surrogate model achieve comparable or superior performance. The superiority of our proposed method SAENAS-NE over other state-of-the-art neural architecture algorithm has been verified in the experiments.<\/jats:p>","DOI":"10.1007\/s40747-022-00929-w","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T14:03:06Z","timestamp":1670248986000},"page":"3313-3331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Surrogate-assisted evolutionary neural architecture search with network embedding"],"prefix":"10.1007","volume":"9","author":[{"given":"Liang","family":"Fan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4805-3780","authenticated-orcid":false,"given":"Handing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"929_CR1","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 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