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Thus, this paper attempts to develop a more efficient NAS method, called EGFA-NAS, by utilizing the work mechanisms of EGFA, which relaxes the search discrete space to a continuous one and then utilizes EGFA and gradient descent to optimize the weights of the candidate architectures in conjunction. To reduce the computational cost, a training strategy by utilizing the population mechanism of EGFA-NAS is proposed. In addition, a weight inheritance strategy for the new generated dust individuals is proposed during the explosion operation to improve performance and efficiency. The performance of EGFA-NAS is investigated in two typical micro search spaces: NAS-Bench-201 and DARTS, and compared with various kinds of state-of-the-art NAS competitors. The experimental results demonstrate that EGFA-NAS is able to match or outperform the state-of-the-art NAS methods on image classification tasks with remarkable efficiency improvement.<\/jats:p>","DOI":"10.1007\/s40747-023-01230-0","type":"journal-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T08:01:31Z","timestamp":1696060891000},"page":"1667-1687","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["EGFA-NAS: a neural architecture search method based on explosion gravitation field algorithm"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4644-3002","authenticated-orcid":false,"given":"Xuemei","family":"Hu","sequence":"first","affiliation":[]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Kangping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"1230_CR1","unstructured":"Simonyan K, Zisserman A (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint, arXiv:1409.1556"},{"key":"1230_CR2","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1007\/978-3-319-46493-0_39","volume-title":"Computer Vision\u2014ECCV 2016","author":"G Huang","year":"2016","unstructured":"Huang G, Sun Y, Liu Z et al (2016) Deep networks with stochastic depth. 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