{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:31:36Z","timestamp":1781717496231,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100013209","name":"Hellenic Foundation for Research and Innovation","doi-asserted-by":"publisher","award":["646"],"award-info":[{"award-number":["646"]}],"id":[{"id":"10.13039\/501100013209","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-05979-8","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T12:49:43Z","timestamp":1618490983000},"page":"899-909","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evolving graph convolutional networks for neural architecture search"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5877-0943","authenticated-orcid":false,"given":"George","family":"Kyriakides","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Margaritis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"5979_CR1","unstructured":"Cai H, Yang J, Zhang W, Han S, Yu Y (2018) Path-level network transformation for efficient architecture search. arXiv:1806.02639"},{"key":"5979_CR2","unstructured":"Chau T, Dudziak \u0141, Abdelfattah MS, Lee R, Kim H, Lane ND (2020) Brp-nas: prediction-based nas using gcns. arXiv:2007.08668"},{"key":"5979_CR3","unstructured":"Deng B, Yan J, Lin D (2017) Peephole: predicting network performance before training. arXiv:1712.03351"},{"key":"5979_CR4","unstructured":"Dong X, Yang Y (2020) Nas-bench-102: Extending the scope of reproducible neural architecture search. arXiv:2001.00326"},{"key":"5979_CR5","doi-asserted-by":"crossref","unstructured":"Gao Y, Yang H, Zhang P, Zhou C, Hu Y (2019) Graphnas: graph neural architecture search with reinforcement learning. arXiv:1904.09981","DOI":"10.24963\/ijcai.2020\/195"},{"key":"5979_CR6","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs"},{"issue":"8","key":"5979_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"5979_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-43505-2","volume-title":"Springer handbook of computational intelligence","author":"J Kacprzyk","year":"2015","unstructured":"Kacprzyk J, Pedrycz W (2015) Springer handbook of computational intelligence. Springer, Berlin"},{"key":"5979_CR9","unstructured":"Kandasamy K, Neiswanger W, Schneider J, Poczos B, Xing EP (2018) Neural architecture search with Bayesian optimisation and optimal transport. arXiv:1802.07191"},{"issue":"1\/2","key":"5979_CR10","doi-asserted-by":"publisher","first-page":"81","DOI":"10.2307\/2332226","volume":"30","author":"MG Kendall","year":"1938","unstructured":"Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1\/2):81\u201393","journal-title":"Biometrika"},{"key":"5979_CR11","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"5979_CR12","unstructured":"Krizhevsky A, Hinton G et\u00a0al. (2009) Learning multiple layers of features from tiny images"},{"issue":"23","key":"5979_CR13","doi-asserted-by":"publisher","first-page":"17321","DOI":"10.1007\/s00521-020-04915-6","volume":"32","author":"G Kyriakides","year":"2020","unstructured":"Kyriakides G, Margaritis K (2020) The effect of reduced training in neural architecture search. Neural Comput Appl 32(23):17321\u201317332","journal-title":"Neural Comput Appl"},{"issue":"11","key":"5979_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"5979_CR15","doi-asserted-by":"crossref","unstructured":"Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li LJ, Fei-Fei L, Yuille A, Huang J, Murphy K (2018) Progressive neural architecture search. http:\/\/github.com\/tensorflow\/","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"5979_CR16","unstructured":"Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K (2018) Hierarchical representations for efficient architecture search. In: 6th international conference on learning representations, ICLR 2018\u2014conference track proceedings. arxiv:1711.00436"},{"issue":"2","key":"5979_CR17","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1049\/ccs.2019.0024","volume":"2","author":"D Long","year":"2019","unstructured":"Long D, Zhang S, Zhang Y (2019) Performance prediction based on neural architecture features. Cogn Comput Syst 2(2):80\u201383","journal-title":"Cogn Comput Syst"},{"key":"5979_CR18","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv:1711.05101"},{"key":"5979_CR19","first-page":"7816","volume":"2018","author":"R Luo","year":"2018","unstructured":"Luo R, Tian F, Qin T, Chen E, Liu TY (2018) Neural architecture optimization. Adv Neural Inf Process Syst 2018:7816\u20137827","journal-title":"Adv Neural Inf Process Syst"},{"key":"5979_CR20","doi-asserted-by":"crossref","unstructured":"Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N et\u00a0al (2019) Evolving deep neural networks. In: Artificial intelligence in the age of neural networks and brain computing. Elsevier, pp 293\u2013312","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"5979_CR21","unstructured":"Pham H, Guan MY, Zoph B, Le QV, Dean J (2018) Efficient neural architecture search via parameter sharing. In: 35th international conference on machine learning, ICML 2018, vol 9, pp. 6522\u20136531. arXiv:1802.03268"},{"key":"5979_CR22","doi-asserted-by":"publisher","first-page":"4780","DOI":"10.1609\/aaai.v33i01.33014780","volume":"33","author":"E Real","year":"2019","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2019) Regularized evolution for image classifier architecture search. Proc AAAI Conf Artif Intell 33:4780\u20134789. https:\/\/doi.org\/10.1609\/aaai.v33i01.33014780","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"5979_CR23","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den\u00a0Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"5979_CR24","doi-asserted-by":"crossref","unstructured":"Smith LN (2017) Cyclical learning rates for training neural networks. In: 2017 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 464\u2013472","DOI":"10.1109\/WACV.2017.58"},{"issue":"2","key":"5979_CR25","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2):99\u2013127","journal-title":"Evol Comput"},{"issue":"2","key":"5979_CR26","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1109\/TEVC.2019.2924461","volume":"24","author":"Y Sun","year":"2019","unstructured":"Sun Y, Wang H, Xue B, Jin Y, Yen GG, Zhang M (2019) Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor. IEEE Trans Evol Comput 24(2):350\u2013364","journal-title":"IEEE Trans Evol Comput"},{"key":"5979_CR27","doi-asserted-by":"crossref","unstructured":"Tang Y, Wang Y, Xu Y, Chen H, Shi B, Xu C, Xu C, Tian Q, Xu C (2020) A semi-supervised assessor of neural architectures. In: IEEE\/CVF conference on computer vision and pattern recognition, pp. 1810\u20131819","DOI":"10.1109\/CVPR42600.2020.00188"},{"key":"5979_CR28","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747"},{"key":"5979_CR29","unstructured":"Xu Y, Wang Y, Han K, Jui S, Xu C, Tian Q, Xu C (2019) Renas: relativistic evaluation of neural architecture search. arXiv:1910.01523"},{"key":"5979_CR30","unstructured":"Ying C, Klein A, Real E, Christiansen E, Murphy K, Hutter F (2019) Nas-bench-101: towards reproducible neural architecture search. arXiv:1902.09635"},{"key":"5979_CR31","unstructured":"Zela A, Siems J, Hutter F (2020) Nas-bench-1shot1: benchmarking and dissecting one-shot neural architecture search. arXiv:2001.10422"},{"key":"5979_CR32","unstructured":"Zhao H, Wei L, Yao Q (2020) Simplifying architecture search for graph neural network. arXiv:2008.11652"},{"key":"5979_CR33","doi-asserted-by":"crossref","unstructured":"Zhong Z, Yan J, Wu W, Shao J, Liu CL (2018) Practical block-wise neural network architecture generation. In: IEEE conference on computer vision and pattern recognition, pp. 2423\u20132432","DOI":"10.1109\/CVPR.2018.00257"},{"key":"5979_CR34","unstructured":"Zhou K, Song Q, Huang X, Hu X (2019) Auto-gnn: neural architecture search of graph neural networks. arXiv:1909.03184"},{"key":"5979_CR35","unstructured":"Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv:1611.01578"},{"key":"5979_CR36","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 8697\u20138710. arXiv:1707.07012","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05979-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05979-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05979-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T15:20:53Z","timestamp":1642778453000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05979-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,15]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["5979"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05979-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,15]]},"assertion":[{"value":"16 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}