{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T10:46:53Z","timestamp":1774262813042,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030504168","type":"print"},{"value":"9783030504175","type":"electronic"}],"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.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\/978-3-030-50417-5_25","type":"book-chapter","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T20:09:52Z","timestamp":1592510992000},"page":"337-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Evolving Long Short-Term Memory Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8593-9583","authenticated-orcid":false,"given":"Vicente Coelho","family":"Lobo Neto","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3529-3109","authenticated-orcid":false,"given":"Leandro Aparecido","family":"Passos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6494-7514","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Papa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"issue":"7553","key":"25_CR1","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.E.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"25_CR2","series-title":"Lecture Notes in Computational Vision and Biomechanics","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-030-32040-9_17","volume-title":"VipIMAGE 2019","author":"LA Passos","year":"2019","unstructured":"Passos, L.A., Santos, C., Pereira, C.R., Afonso, L.C.S., Papa, J.P.: A hybrid approach for breast mass categorization. In: Tavares, J.M.R.S., Natal Jorge, R.M. (eds.) VipIMAGE 2019. LNCVB, vol. 34, pp. 159\u2013168. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32040-9_17"},{"key":"25_CR3","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/MIS.2019.2949984","volume":"35","author":"MC Santana","year":"2019","unstructured":"Santana, M.C., Passos, L.A., Moreira, T., Colombo, D., De Albuquerque, V.H.C., Papa, J.P.: A novel siamese-based approach for scene change detection with applications to obstructed routes in hazardous environments. IEEE Intell. Syst. 35, 44\u201353 (2019). https:\/\/doi.org\/10.1109\/MIS.2019.2949984","journal-title":"IEEE Intell. Syst."},{"issue":"8","key":"25_CR4","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554\u20132558 (1982)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"25_CR5","doi-asserted-by":"publisher","first-page":"103477","DOI":"10.1016\/j.compbiomed.2019.103477","volume":"115","author":"LC Ribeiro","year":"2019","unstructured":"Ribeiro, L.C., Afonso, L.C., Papa, J.P.: Bag of samplings for computer-assisted Parkinson\u2019s disease diagnosis based on recurrent neural networks. Comput. Biol. Med. 115, 103477 (2019)","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"25_CR6","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."},{"key":"25_CR7","unstructured":"Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF, CoRR abs\/1603.01354"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Komninos, A., Manandhar, S.: Dependency based embeddings for sentence classification tasks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1490\u20131500 (2016)","DOI":"10.18653\/v1\/N16-1175"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association, pp. 338\u2013342 (2014)","DOI":"10.21437\/Interspeech.2014-80"},{"key":"25_CR10","unstructured":"Greff, K., Srivastava, R.K., Koutn\u00edk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey, CoRR abs\/1503.04069"},{"key":"25_CR11","unstructured":"Reimers, N., Gurevych, I.: Optimal hyperparameters for deep LSTM-networks for sequence labeling tasks. CoRR abs\/1707.06799"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Fedorovici, L., Precup, R., Dragan, F., David, R., Purcaru, C.: Embedding gravitational search algorithms in convolutional neural networks for OCR applications. In: 7th IEEE International Symposium on Applied Computational Intelligence and Informatics. SACI 2012, pp. 125\u2013130 (2012). https:\/\/doi.org\/10.1109\/SACI.2012.6249989","DOI":"10.1109\/SACI.2012.6249989"},{"issue":"13","key":"25_CR13","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232\u20132248 (2009)","journal-title":"Inf. Sci."},{"issue":"11","key":"25_CR14","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)","journal-title":"Proc. IEEE"},{"key":"25_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/978-3-319-25751-8_82","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"G Rosa","year":"2015","unstructured":"Rosa, G., Papa, J., Marana, A., Scheirer, W., Cox, D.: Fine-tuning convolutional neural networks using harmony search. CIARP 2015. LNCS, vol. 9423, pp. 683\u2013690. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25751-8_82"},{"key":"25_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00185-7","volume-title":"Music-Inspired Harmony Search Algorithm: Theory and Applications","author":"ZW Geem","year":"2009","unstructured":"Geem, Z.W.: Music-Inspired Harmony Search Algorithm: Theory and Applications, 1st edn. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-00185-7","edition":"1"},{"key":"25_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/978-3-319-46182-3_12","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"G Rosa","year":"2016","unstructured":"Rosa, G., Papa, J., Costa, K., Passos, L., Pereira, C., Yang, X.-S.: Learning parameters in deep belief networks through firefly algorithm. In: Schwenker, F., Abbas, H.M., El Gayar, N., Trentin, E. (eds.) ANNPR 2016. LNCS (LNAI), vol. 9896, pp. 138\u2013149. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46182-3_12"},{"issue":"2","key":"25_CR18","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","volume":"2","author":"X-S Yang","year":"2010","unstructured":"Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78\u201384 (2010)","journal-title":"Int. J. Bio-Inspired Comput."},{"issue":"7","key":"25_CR19","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527\u20131554 (2006)","journal-title":"Neural Comput."},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Passos, L.A., Rodrigues, D.R., Papa, J.P., Fine tuning deep Boltzmann machines through meta-heuristic approaches. In: 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 000419\u2013000424. IEEE (2018)","DOI":"10.1109\/SACI.2018.8440959"},{"issue":"1","key":"25_CR21","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11063-017-9707-2","volume":"48","author":"LA Passos","year":"2018","unstructured":"Passos, L.A., Papa, J.P.: Temperature-based deep Boltzmann machines. Neural Process. Lett. 48(1), 95\u2013107 (2018). https:\/\/doi.org\/10.1007\/s11063-017-9707-2","journal-title":"Neural Process. Lett."},{"issue":"8","key":"25_CR22","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1162\/NECO_a_00311","volume":"24","author":"R Salakhutdinov","year":"2012","unstructured":"Salakhutdinov, R., Hinton, G.E.: An efficient learning procedure for deep Boltzmann machines. Neural Comput. 24(8), 1967\u20132006 (2012)","journal-title":"Neural Comput."},{"issue":"10","key":"25_CR23","doi-asserted-by":"publisher","first-page":"3765","DOI":"10.3390\/su10103765","volume":"10","author":"H Chung","year":"2018","unstructured":"Chung, H., Shin, K.-S.: Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability 10(10), 3765 (2018)","journal-title":"Sustainability"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Kim, T., Cho, S.: Particle swarm optimization-based CNN-LSTM networks for forecasting energy consumption. In: 2019 IEEE Congress on Evolutionary Computation, pp. 1510\u20131516 (2019)","DOI":"10.1109\/CEC.2019.8789968"},{"key":"25_CR25","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1023\/A:1022602019183","volume":"3","author":"DE Goldberg","year":"1988","unstructured":"Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3, 95\u201399 (1988)","journal-title":"Mach. Learn."},{"key":"25_CR26","volume-title":"Genetic Programming: On the Programming of Computers by Means of Natural Selection","author":"JR Koza","year":"1992","unstructured":"Koza, J.R., Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)"},{"key":"25_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-642-32937-1_3","volume-title":"Parallel Problem Solving from Nature - PPSN XII","author":"A Moraglio","year":"2012","unstructured":"Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 21\u201331. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32937-1_3"},{"key":"25_CR28","doi-asserted-by":"publisher","unstructured":"Perkis, T.: Stack-based genetic programming. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, vol. 1, pp. 148\u2013153 (1994). https:\/\/doi.org\/10.1109\/ICEC.1994.350025","DOI":"10.1109\/ICEC.1994.350025"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Brill, E.: A simple rule-based part of speech tagger. In: Proceedings of the Third Conference on Applied Natural Language Processing, pp. 152\u2013155. Association for Computational Linguistics (1992)","DOI":"10.3115\/974499.974526"},{"issue":"3","key":"25_CR30","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/0885-2308(92)90019-Z","volume":"6","author":"J Kupiec","year":"1992","unstructured":"Kupiec, J.: Robust part-of-speech tagging using a hidden Markov model. Comput. Speech Lang. 6(3), 225\u2013242 (1992)","journal-title":"Comput. Speech Lang."},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. In: 9th International Conference on Artificial Neural Networks: ICANN 1999, pp. 850\u2013855. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale (1999)","DOI":"10.1049\/cp:19991218"},{"key":"25_CR32","doi-asserted-by":"publisher","unstructured":"Loper, E., Bird, S.: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1. ETMTNLP 2002, pp. 63\u201370. Association for Computational Linguistics, Stroudsburg (2002). https:\/\/doi.org\/10.3115\/1118108.1118117","DOI":"10.3115\/1118108.1118117"},{"issue":"6","key":"25_CR33","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80\u201383 (1945)","journal-title":"Biom. Bull."},{"key":"25_CR34","unstructured":"Chollet, F., et al.: Keras. GitHub (2015). https:\/\/github.com\/fchollet\/keras"},{"key":"25_CR35","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems, software available from tensorflow.org (2015). http:\/\/tensorflow.org\/"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50417-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T23:13:03Z","timestamp":1718665983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-50417-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030504168","9783030504175"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50417-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","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":"3 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2020\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"230","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":"98","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":"3","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":"43% - 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":"2.5","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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248 workshop papers were selected from 489 submissions to the thematic tracks. The conference was canceled due to the COVID-19 pandemic.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}