{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:27:15Z","timestamp":1743024435522,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030437213"},{"type":"electronic","value":"9783030437220"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-43722-0_39","type":"book-chapter","created":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T23:04:05Z","timestamp":1586387045000},"page":"610-625","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Neuro-Evolutionary Transfer Learning Through Structural Adaptation"],"prefix":"10.1007","author":[{"given":"AbdElRahman","family":"ElSaid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Karnas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zimeng","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Krutz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander G.","family":"Ororbia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Travis","family":"Desell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"39_CR1","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"39_CR2","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"39_CR3","unstructured":"Moreno, G.A., C\u00e1mara, J., Garlan, D., Schmerl, B.: Proactive self-adaptation under uncertainty: a probabilistic model checking approach. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC\/FSE 2015, pp. 1\u201312. ACM, New York (2015). http:\/\/doi.acm.org\/10.1145\/2786805.2786853"},{"key":"39_CR4","unstructured":"Moreno, G.A.: Adaptation timing in self-adaptive systems. Ph.D. thesis, Carnegie Mellon University (2017)"},{"key":"39_CR5","unstructured":"Palmerino, J., Yu, Q., Desell, T., Krutz, D.: Accounting for tactic volatility in self-adaptive systems for improved decision-making. In: Proceedings of the 34th ACM\/IEEE International Conference on Automated Software Engineering. ASE 2019. ACM, New York (2019)"},{"key":"39_CR6","unstructured":"Gupta, P., Malhotra, P., Vig, L., Shroff, G.: Transfer learning for clinical time series analysis using recurrent neural networks. arXiv preprint arXiv:1807.01705 (2018)"},{"issue":"12","key":"39_CR7","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.3390\/app8122416","volume":"8","author":"A Zhang","year":"2018","unstructured":"Zhang, A., et al.: Transfer learning with deep recurrent neural networks for remaining useful life estimation. Appl. Sci. 8(12), 2416 (2018)","journal-title":"Appl. Sci."},{"key":"39_CR8","unstructured":"Yoon, S., Yun, H., Kim, Y., Park, G.T., Jung, K.: Efficient transfer learning schemes for personalized language modeling using recurrent neural network. In: Workshops at the Thirty-First AAAI Conference on Artificial Intelligence (2017)"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Zarrella, G., Marsh, A.: MITRE at SemEval-2016 task 6: transfer learning for stance detection. arXiv preprint arXiv:1606.03784 (2016)","DOI":"10.18653\/v1\/S16-1074"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Mrk\u0161i\u0107, N., et al.: Multi-domain dialog state tracking using recurrent neural networks. arXiv preprint arXiv:1506.07190 (2015)","DOI":"10.3115\/v1\/P15-2130"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Mun, S., Shon, S., Kim, W., Han, D.K., Ko, H.: Deep neural network based learning and transferring mid-level audio features for acoustic scene classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 796\u2013800. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952265"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Taylor, M.E., Whiteson, S., Stone, P.: Transfer via inter-task mappings in policy search reinforcement learning. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, p. 37. ACM (2007)","DOI":"10.1145\/1329125.1329170"},{"key":"39_CR13","unstructured":"Yang, Z., Salakhutdinov, R., Cohen, W.W.: Transfer learning for sequence tagging with hierarchical recurrent networks. arXiv preprint arXiv:1703.06345 (2017)"},{"issue":"May","key":"39_CR14","first-page":"1737","volume":"11","author":"P Verbancsics","year":"2010","unstructured":"Verbancsics, P., Stanley, K.O.: Evolving static representations for task transfer. J. Mach. Learn. Res. 11(May), 1737\u20131769 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"39_CR15","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Tang, Z., Wang, D., Zhang, Z.: Recurrent neural network training with dark knowledge transfer. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5900\u20135904. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7472809"},{"key":"39_CR17","unstructured":"Deo, R.V., Chandra, R., Sharma, A.: Stacked transfer learning for tropical cyclone intensity prediction. arXiv preprint arXiv:1708.06539 (2017)"},{"key":"39_CR18","unstructured":"Ororbia, A., ElSaid, A., Desell, T.: Investigating recurrent neural network memory structures using neuro-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp. 446\u2013455. ACM, New York (2019). http:\/\/doi.acm.org\/10.1145\/3321707.3321795"},{"issue":"12","key":"39_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/neco_a_01017","volume":"29","author":"AG Ororbia II","year":"2017","unstructured":"Ororbia II, A.G., Mikolov, T., Reitter, D.: Learning simpler language models with the differential state framework. Neural Comput. 29(12), 1\u201326 (2017). https:\/\/doi.org\/10.1162\/neco_a_01017. PMID: 28957029","journal-title":"Neural Comput."},{"key":"39_CR20","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"issue":"8","key":"39_CR21","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.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"3","key":"39_CR22","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/s11633-016-1006-2","volume":"13","author":"G-B Zhou","year":"2016","unstructured":"Zhou, G.-B., Wu, J., Zhang, C.-L., Zhou, Z.-H.: Minimal gated unit for recurrent neural networks. Int. J. Autom. Comput. 13(3), 226\u2013234 (2016). https:\/\/doi.org\/10.1007\/s11633-016-1006-2","journal-title":"Int. J. Autom. Comput."},{"key":"39_CR23","unstructured":"Collins, J., Sohl-Dickstein, J., Sussillo, D.: Capacity and trainability in recurrent neural networks. arXiv preprint arXiv:1611.09913 (2016)"},{"key":"39_CR24","unstructured":"Message Passing Interface Forum: MPI: A message-passing interface standard. The International Journal of Supercomputer Applications and High Performance Computing 8(3\/4), 159\u2013416 (Fall\/Winter 1994)"},{"issue":"10","key":"39_CR25","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"PJ Werbos","year":"1990","unstructured":"Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550\u20131560 (1990)","journal-title":"Proc. IEEE"},{"key":"39_CR26","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1016\/j.asoc.2018.09.013","volume":"73","author":"A ElSaid","year":"2018","unstructured":"ElSaid, A., El Jamiy, F., Higgins, J., Wild, B., Desell, T.: Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration. Appl. Soft Comput. 73, 969\u2013991 (2018)","journal-title":"Appl. Soft Comput."},{"key":"39_CR27","unstructured":"Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: International Conference on Machine Learning, pp. 2342\u20132350 (2015)"},{"key":"39_CR28","unstructured":"Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310\u20131318 (2013)"}],"container-title":["Lecture Notes in Computer Science","Applications of Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-43722-0_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T20:29:09Z","timestamp":1614889749000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-43722-0_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030437213","9783030437220"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-43722-0_39","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":"9 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoApplications","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Applications of Evolutionary Computation (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"15 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evoapplications2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","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":"44","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":"0","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":"71% - 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":"2.68","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":"1.25","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":"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)"}}]}}