{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T10:46:10Z","timestamp":1780656370885,"version":"3.54.1"},"reference-count":35,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":[[2022,7,18]]},"DOI":"10.1109\/cec55065.2022.9870442","type":"proceedings-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T19:28:14Z","timestamp":1662492494000},"page":"1-9","source":"Crossref","is-referenced-by-count":7,"title":["A conjugated evolutionary algorithm for hyperparameter optimization"],"prefix":"10.1109","author":[{"given":"Luis","family":"Japa","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Kumamoto University,Kumamoto,Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcello","family":"Serqueira","sequence":"additional","affiliation":[{"name":"Federal Center for Technological Education of Rio de Janeiro,Department of Computer Science,Rio de Janeiro,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Israel","family":"Mendonca","sequence":"additional","affiliation":[{"name":"Kumamoto University,Faculty of Advanced Science and Technology,Kumamoto,Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Bezerra","sequence":"additional","affiliation":[{"name":"Federal Center for Technological Education of Rio de Janeiro,Department of Computer Science,Rio de Janeiro,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayoshi","family":"Aritsugi","sequence":"additional","affiliation":[{"name":"Kumamoto University,Faculty of Advanced Science and Technology,Kumamoto,Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro Henrique","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Federal Center for Technological Education of Rio de Janeiro,Department of Computer Science,Rio de Janeiro,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2019.11.037"},{"key":"ref32","author":"han","year":"2011","journal-title":"Data Mining Concepts and Techniques"},{"key":"ref31","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"3rd International Conference on Learning Representations ICLR 2015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727189"},{"key":"ref35","author":"nogueira","year":"2014","journal-title":"Bayesian Optimization Open source constrained global optimization tool for Python"},{"key":"ref34","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":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273556"},{"key":"ref11","first-page":"281","article-title":"Random search for hyper-parameter opti-mization","volume":"13","author":"bergstra","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref12","article-title":"Random search algorithms","author":"zabinsky","year":"2009","journal-title":"Wiley Encyclopedia of Operations Research and Management Science"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-5362-2_5"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-010-9143-1"},{"key":"ref15","author":"bischl","year":"2021","journal-title":"Hyperparameter optimization Foundations algorithms best practices and open challenges"},{"key":"ref16","first-page":"2171","article-title":"Scalable bayesian optimization using deep neural networks","author":"snoek","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref17","author":"loshchilov","year":"2016","journal-title":"CMA-ES for hyperparameter optimization of deep neural networks"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/HiPC.2018.00014"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2019.2918509"},{"key":"ref28","author":"krizhevsky","year":"0","journal-title":"Cifar-10 (canadian institute for advanced research)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-32494-1_4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1086\/516585"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2012.07.018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2576768.2598294"},{"key":"ref29","author":"lecun","year":"1998","journal-title":"The MNIST Database of Handwritten Digits"},{"key":"ref5","author":"hansen","year":"2016","journal-title":"The CMA evolution strategy A tutorial"},{"key":"ref8","first-page":"2951","article-title":"Practical bayesian optimization of machine learning algorithms","author":"snoek","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref7","article-title":"A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning","volume":"abs 1012 2599","author":"brochu","year":"2010","journal-title":"CoRR"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022602019183"},{"key":"ref9","first-page":"120","volume":"44","author":"barbero","year":"2007","journal-title":"Finding Optimal Model Parameters by Discrete Grid Search"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.11.062"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2018.8477947"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.entcs.2011.11.026"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2004.09.057"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2008.01.005"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0255(94)90123-6"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.6.2.154"}],"event":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 IEEE Congress on Evolutionary Computation (CEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9870216\/9870201\/09870442.pdf?arnumber=9870442","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T21:15:11Z","timestamp":1664226911000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9870442\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/cec55065.2022.9870442","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}