{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T15:01:19Z","timestamp":1725980479667},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319937007"},{"type":"electronic","value":"9783319937014"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-93701-4_41","type":"book-chapter","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T11:49:52Z","timestamp":1528717792000},"page":"528-539","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimising Deep Learning by Hyper-heuristic Approach for Classifying Good Quality Images"],"prefix":"10.1007","author":[{"given":"Muneeb ul","family":"Hassan","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0276-4704","authenticated-orcid":false,"given":"Nasser R.","family":"Sabar","sequence":"additional","affiliation":[]},{"given":"Andy","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,6,12]]},"reference":[{"issue":"7587","key":"41_CR1","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"issue":"1","key":"41_CR3","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji, S., Wei, X., Yang, M., Kai, Y.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221\u2013231 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"41_CR4","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725\u20131732 (2014)","DOI":"10.1109\/CVPR.2014.223"},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (icassp), pp. 6645\u20136649. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"41_CR6","unstructured":"Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., Blunsom, P.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, pp. 1693\u20131701 (2015)"},{"issue":"11","key":"41_CR7","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":"41_CR8","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"41_CR9","first-page":"1","volume":"45","author":"S Basu","year":"2015","unstructured":"Basu, S., Karki, M., Ganguly, S., DiBiano, R., Mukhopadhyay, S., Gayaka, S., Kannan, R., Nemani, R.: Learning sparse feature representations using probabilistic quadtrees and deep belief nets. Neural Process. Lett. 45, 1\u201313 (2015)","journal-title":"Neural Process. Lett."},{"issue":"10","key":"41_CR10","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1016\/S0031-3203(03)00085-2","volume":"36","author":"C-L Liu","year":"2003","unstructured":"Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recogn. 36(10), 2271\u20132285 (2003)","journal-title":"Pattern Recogn."},{"key":"41_CR11","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)"},{"key":"41_CR12","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/978-1-4419-1665-5_15","volume-title":"Handbook of Metaheuristics","author":"EK Burke","year":"2010","unstructured":"Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., \u00d6zcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, pp. 449\u2013468. Springer, Boston (2010). https:\/\/doi.org\/10.1007\/978-1-4419-1665-5_15"},{"key":"41_CR13","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.ins.2014.10.045","volume":"314","author":"NR Sabar","year":"2015","unstructured":"Sabar, N.R., Kendall, G.: Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems. Inf. Sci. 314, 225\u2013239 (2015)","journal-title":"Inf. Sci."},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Sabar, N.R., Zhang, X.J., Song, A.: A math-hyper-heuristic approach for large-scale vehicle routing problems with time windows. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 830\u2013837. IEEE (2015)","DOI":"10.1109\/CEC.2015.7256977"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Sabar, N.R., Ayob, M.: Examination timetabling using scatter search hyper-heuristic. In: 2nd Conference on Data Mining and Optimization 2009, DMO 2009, pp. 127\u2013131. IEEE (2009)","DOI":"10.1109\/DMO.2009.5341899"},{"key":"41_CR16","first-page":"4","volume":"3","author":"NR Sabar","year":"2012","unstructured":"Sabar, N.R., Ayob, M., Kendall, G., Qu, R.: Grammatical evolution hyper-heuristic for combinatorial optimization problems. Strategies 3, 4 (2012)","journal-title":"Strategies"},{"issue":"3","key":"41_CR17","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/TEVC.2014.2319051","volume":"19","author":"NR Sabar","year":"2015","unstructured":"Sabar, N.R., Ayob, M., Kendall, G., Qu, R.: Automatic design of a hyper-heuristic framework with gene expression programming for combinatorial optimization problems. IEEE Trans. Evol. Comput. 19(3), 309\u2013325 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Abdullah, S., Sabar, N.R., Nazri, M.Z.A., Turabieh, H., McCollum, B.: A constructive hyper-heuristics for rough set attribute reduction. In: 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 1032\u20131035. IEEE (2010)","DOI":"10.1109\/ISDA.2010.5687052"},{"issue":"2","key":"41_CR19","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TCYB.2014.2323936","volume":"45","author":"NR Sabar","year":"2015","unstructured":"Sabar, N.R., Ayob, M., Kendall, G., Qu, R.: A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems. IEEE Trans. Cybern. 45(2), 217\u2013228 (2015)","journal-title":"IEEE Trans. Cybern."},{"issue":"2","key":"41_CR20","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TEVC.2016.2602860","volume":"21","author":"NR Sabar","year":"2017","unstructured":"Sabar, N.R., Abawajy, J., Yearwood, J.: Heterogeneous cooperative co-evolution memetic differential evolution algorithm for big data optimization problems. IEEE Trans. Evol. Comput. 21(2), 315\u2013327 (2017)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Sabar, N.R., Turky, A.M., Song, A.: Optimising deep belief networks by hyper-heuristic approach. In: CEC 2017-IEEE Congress on Evolutionary Computation (2017)","DOI":"10.1109\/CEC.2017.7969640"},{"key":"41_CR22","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-93701-4_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:15:32Z","timestamp":1654906532000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-93701-4_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319937007","9783319937014"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-93701-4_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 June 2018","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":"Wuxi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2018\/","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":"406","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":"148","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":"60","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":"36% - 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","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","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}