{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T09:47:30Z","timestamp":1749548850829,"version":"3.28.0"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,28]]},"DOI":"10.1109\/cec45853.2021.9504850","type":"proceedings-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T21:57:06Z","timestamp":1628546226000},"page":"79-86","source":"Crossref","is-referenced-by-count":4,"title":["gaCNN: Composing CNNs and GAs to Build an Optimized Hybrid Classification Architecture"],"prefix":"10.1109","author":[{"given":"Raphael","family":"de Lima Mendes","sequence":"first","affiliation":[{"name":"Federal University of Uberlandia (UFU),Faculty of Computation (FACOM),Uberlandia,Minas Gerais,Brazil"}]},{"given":"Alexandre Henrick","family":"da Silva Alves","sequence":"additional","affiliation":[{"name":"Federal University of Uberlandia (UFU),Faculty of Computation (FACOM),Uberlandia,Minas Gerais,Brazil"}]},{"given":"Matheus","family":"de Souza Gomes","sequence":"additional","affiliation":[{"name":"Federal University of Uberlandia (UFU),Faculty of Computation (FACOM),Patos de Minas,Minas Gerais,Brazil"}]},{"given":"Pedro","family":"Luiz Lima Bertarini","sequence":"additional","affiliation":[{"name":"Federal University of Uberlandia (UFU),Institute of Biotechnology (IBTEC),Patos de Minas,Minas Gerais,Brazil"}]},{"given":"Laurence Rodrigues","family":"do Amaral","sequence":"additional","affiliation":[{"name":"Federal University of Uberlandia (UFU),Faculty of Electrical Engineering (FEELT),Patos de Minas,Minas Gerais,Brazil"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2018.8477735"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280683"},{"journal-title":"Fashion-mnist a novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"xiao","key":"ref32"},{"article-title":"MNIST handwritten digit database","year":"2010","author":"lecun","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2475625"},{"article-title":"Contractive auto-encoders: Explicit invariance during feature extraction","year":"2011","author":"rifai","key":"ref36"},{"key":"ref35","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref34","article-title":"Handwritten digit recognition using convolutional neural networks and gabor filters","author":"calder\u00f3n","year":"2003","journal-title":"Proc Int Congr Comput Intell"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2014.10.008"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2008.919004"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2006.329691"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8191(90)90086-O"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.100561"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2019.8789964"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1162\/106365602320169811"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2018.8477728"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2019.8790145"},{"article-title":"A survey on neural architecture search","year":"2019","author":"wistuba","key":"ref19"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref27","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref3","article-title":"Convolutional networks for images, speech, and time series","author":"lecun","year":"1995","journal-title":"The Handbook of Brain Theory and Neural Networks"},{"key":"ref6","first-page":"2","article-title":"Learning and relearning in boltzmann machines","volume":"1","author":"hinton","year":"1986","journal-title":"Parallel Distributed Processing Explorations in the Microstructure of Cognition"},{"year":"0","key":"ref29","article-title":"Random depthwise signed convolutional neural networks"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690370209"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0792-66"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.05.010"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref2"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02478259"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"article-title":"Designing neural network architectures using reinforcement learning","year":"2016","author":"baker","key":"ref21"},{"article-title":"Cifar-10 (canadian institute for advanced research)","year":"0","author":"krizhevsky","key":"ref42"},{"key":"ref24","first-page":"243","article-title":"Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations","author":"wistuba","year":"2018","journal-title":"Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases"},{"key":"ref41","first-page":"562","article-title":"Deeply-supervised nets","author":"lee","year":"2015","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref23","first-page":"2902","article-title":"Large-scale evolution of image classifiers","author":"real","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning- Volume 70"},{"year":"0","key":"ref26","article-title":"Understanding convolution neural networks: Cnn— the eli5 way"},{"article-title":"Cifar-100 (canadian institute for advanced research)","year":"0","author":"krizhevsky","key":"ref43"},{"key":"ref25","article-title":"Evolving deep convolutional neural networks for image classification","author":"sun","year":"2019","journal-title":"IEEE Transactions on Evolutionary Computation"}],"event":{"name":"2021 IEEE Congress on Evolutionary Computation (CEC)","start":{"date-parts":[[2021,6,28]]},"location":"Krak\u00f3w, Poland","end":{"date-parts":[[2021,7,1]]}},"container-title":["2021 IEEE Congress on Evolutionary Computation (CEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9504682\/9504683\/09504850.pdf?arnumber=9504850","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:36:20Z","timestamp":1659483380000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9504850\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/cec45853.2021.9504850","relation":{},"subject":[],"published":{"date-parts":[[2021,6,28]]}}}