{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T01:50:04Z","timestamp":1726105804977},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030638351"},{"type":"electronic","value":"9783030638368"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63836-8_23","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T09:12:07Z","timestamp":1605690727000},"page":"270-281","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-objective Evolution for Deep Neural Network Architecture Search"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3879-3459","authenticated-orcid":false,"given":"Petra","family":"Vidnerov\u00e1","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2364-5357","authenticated-orcid":false,"given":"Roman","family":"Neruda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"23_CR1","unstructured":"Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013) (2013)"},{"key":"23_CR2","unstructured":"Chollet, F.: Keras (2015). https:\/\/github.com\/fchollet\/keras"},{"issue":"4","key":"23_CR3","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-points-based nondominated sorting approach. IEEE Trans. Evol. Comput. 18(4), 577\u2013601 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"23_CR4","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002). https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Efficient multi-objective neural architecture search via Lamarckian evolution. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=ByME42AqK7","DOI":"10.1007\/978-3-030-05318-5_3"},{"issue":"55","key":"23_CR6","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1\u201321 (2019). http:\/\/jmlr.org\/papers\/v20\/18-598.html","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Gong, C., Jiang, Z., Wang, D., Lin, Y., Liu, Q., Pan, D.Z.: Mixed precision neural architecture search for energy efficient deep learning. In: 2019 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1\u20137 (2019)","DOI":"10.1109\/ICCAD45719.2019.8942147"},{"key":"23_CR8","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http:\/\/www.deeplearningbook.org"},{"key":"23_CR9","unstructured":"Guo, Y., et al.: NAT: neural architecture transformer for accurate and compact architectures. In: NeurIPS (2019)"},{"key":"23_CR10","unstructured":"Goodfellow, I., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org. https:\/\/www.tensorflow.org\/"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Jin, H., Song, Q., Hu, X.: Auto-Keras: an efficient neural architecture search system. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1946\u20131956. ACM (2019)","DOI":"10.1145\/3292500.3330648"},{"key":"23_CR12","unstructured":"Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization. http:\/\/maxpumperla.com\/hyperas\/"},{"key":"23_CR13","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: CIFAR-10 (Canadian Institute for Advanced Research). http:\/\/www.cs.toronto.edu\/kriz\/cifar.html"},{"issue":"7553","key":"23_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"issue":"11","key":"23_CR15","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.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"23_CR16","unstructured":"LeCun, Y., Cortes, C.: The MNIST database of handwritten digits (2012). http:\/\/research.microsoft.com\/apps\/pubs\/default.aspx?id=204699"},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Lu, Z., Deb, K., Boddeti, V.: MUXConv: information multiplexing in convolutional neural networks, pp. 12041\u201312050, June 2020. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01206","DOI":"10.1109\/CVPR42600.2020.01206"},{"key":"23_CR18","unstructured":"Miikkulainen, R., et al.: Evolving deep neural networks. CoRR abs\/1703.00548 (2017). http:\/\/arxiv.org\/abs\/1703.00548"},{"issue":"2","key":"23_CR19","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99\u2013127 (2002). http:\/\/nn.cs.utexas.edu\/?stanley:ec02","journal-title":"Evol. Comput."},{"key":"23_CR20","unstructured":"Autonomio Talos [computer software] (2019). http:\/\/github.com\/autonomio\/talos"},{"key":"23_CR21","unstructured":"Vidnerov\u00e1, P., Neruda, R.: Evolution strategies for deep neural network models design. In: Hlav\u00e1\u010dov\u00e1, J. (ed.) Proceedings ITAT 2017: Information Technologies - Applications and Theory. CEUR Workshop Proceedings, vol. 1885, pp. 159\u2013166. Technical University & CreateSpace Independent Publishing Platform, Aachen & Charleston (2017)"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Vidnerov\u00e1, P., Neruda, R.: Evolving Keras architectures for sensor data analysis. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 109\u2013112, September 2017. https:\/\/doi.org\/10.15439\/2017F241","DOI":"10.15439\/2017F241"},{"key":"23_CR23","unstructured":"Vidnerov\u00e1, P., Proch\u00e1zka, \u0160.: NSGA-Keras: Neural architecture search for Keras sequential models (2020). https:\/\/github.com\/PetraVidnerova\/nsga-keras"},{"key":"23_CR24","unstructured":"Vidnerov\u00e1, P., Neruda, R.: Asynchronous evolution of convolutional networks. In: Krajci, S. (ed.) Proceedings of the 18th Conference Information Technologies - Applications and Theory (ITAT 2018), Hotel Plejsy, Slovakia, 21\u201325 September 2018. CEUR Workshop Proceedings, vol. 2203, pp. 80\u201385. CEUR-WS.org (2018). http:\/\/ceur-ws.org\/Vol-2203\/80.pdf"},{"key":"23_CR25","unstructured":"Xia, W., Yin, H., Jha, N.: Efficient synthesis of compact deep neural networks. CoRR abs\/2004.08704 (2020). http:\/\/arxiv.org\/abs\/2004.08704"},{"key":"23_CR26","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (2017)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63836-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:31:25Z","timestamp":1710250285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63836-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030638351","9783030638368"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63836-8_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangkok","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apnns.org\/ICONIP2020","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"618","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"187","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"189","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.18","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.68","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}