{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:40:37Z","timestamp":1792082437963,"version":"4.4.0"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Marsden Fund of New Zealand Government","award":["VUW1913"],"award-info":[{"award-number":["VUW1913"]}]},{"name":"Marsden Fund of New Zealand Government","award":["VUW1914"],"award-info":[{"award-number":["VUW1914"]}]},{"name":"Marsden Fund of New Zealand Government","award":["VUW2115"],"award-info":[{"award-number":["VUW2115"]}]},{"name":"MBIE Data Science SSIF Fund","award":["RTVU1914"],"award-info":[{"award-number":["RTVU1914"]}]},{"name":"Huayin Medical","award":["E3791\/4165"],"award-info":[{"award-number":["E3791\/4165"]}]},{"name":"MBIE Endeavor Research Programme","award":["C11X2001"],"award-info":[{"award-number":["C11X2001"]}]},{"name":"MBIE Endeavor Research Programme","award":["UOCX2104"],"award-info":[{"award-number":["UOCX2104"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Evol. Computat."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1109\/tevc.2023.3245322","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T17:53:49Z","timestamp":1677174829000},"page":"1190-1204","source":"Crossref","is-referenced-by-count":39,"title":["Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-Based Encoding Strategy"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-8807","authenticated-orcid":false,"given":"Gonglin","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9417-1956","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4865-8026","authenticated-orcid":false,"given":"Bing","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4463-9538","authenticated-orcid":false,"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref4","article-title":"DARTS: Differentiable architecture search","author":"Liu","year":"2018","journal-title":"arXiv:1806.09055"},{"key":"ref5","article-title":"Designing neural network architectures using reinforcement learning","author":"Baker","year":"2016","journal-title":"arXiv:1611.02167"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11709"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"ref8","article-title":"Hierarchical representations for efficient architecture search","author":"Liu","year":"2017","journal-title":"arXiv:1711.00436"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.154"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2018.8477933"},{"key":"ref11","article-title":"Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning","author":"Such","year":"2017","journal-title":"arXiv:1712.06567"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3100554"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3220747"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"1","key":"ref15","first-page":"8","article-title":"Particle swarm optimization","volume":"2","author":"Shi","year":"2004","journal-title":"IEEE Connect."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2018.8477735"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3054400"},{"key":"ref18","first-page":"3","article-title":"Autoencoders, minimum description length and Helmholtz free energy","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"6","author":"Hinton"},{"key":"ref19","first-page":"10881","article-title":"Graph structure of neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"You"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2021.3084415"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31129-2_43"},{"key":"ref23","article-title":"Automatically searching for u-net image translator architecture","author":"Shu","year":"2020","journal-title":"arXiv:2002.11581"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-55789-8_61"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CEC48606.2020.9185541"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2695223"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01141"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2916183"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03991-2_24"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2881143"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1611.01578"},{"key":"ref32","first-page":"7105","article-title":"NAS-bench-101: Towards reproducible neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ying"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/IVCNZ51579.2020.9290492"},{"key":"ref34","article-title":"NPENAS: Neural predictor guided evolution for neural architecture search","author":"Wei","year":"2020","journal-title":"arXiv:2003.12857"},{"key":"ref35","first-page":"12486","article-title":"Does unsupervised architecture representation learning help neural architecture search?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Yan"},{"key":"ref36","article-title":"Nasgem: Neural architecture search via graph embedding method","author":"Cheng","year":"2020","journal-title":"arXiv:2007.04452"},{"key":"ref37","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Real"},{"key":"ref38","article-title":"Evolutionary neural architecture search for retinal vessel segmentation","author":"Fan","year":"2020","journal-title":"arXiv:2001.06678"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01202"},{"key":"ref40","article-title":"Variational lossy autoencoder","author":"Chen","year":"2016","journal-title":"arXiv:1611.02731"},{"key":"ref41","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref43","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1605.07648"},{"key":"ref46","first-page":"1319","article-title":"Maxout networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Goodfellow"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1505.00387"},{"key":"ref48","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071229"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00257"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00927"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2983860"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2919608"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-77553-1_2"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3321707.3321729"},{"key":"ref58","article-title":"Efficient multi-objective neural architecture search via lamarckian evolution","author":"Elsken","year":"2018","journal-title":"arXiv:1804.09081"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2020.3024708"},{"key":"ref60","first-page":"1","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Cai"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2924461"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref63","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2005.02.003"},{"key":"ref65","first-page":"4095","article-title":"Efficient neural architecture search via parameter sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pham"}],"container-title":["IEEE Transactions on Evolutionary Computation"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4235\/10703195\/10045029.pdf?arnumber=10045029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T02:09:13Z","timestamp":1727921353000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10045029\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":65,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tevc.2023.3245322","relation":{},"ISSN":["1089-778X","1089-778X","1941-0026"],"issn-type":[{"value":"1089-778X","type":"print"},{"value":"1089-778X","type":"print"},{"value":"1941-0026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}