{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T19:09:21Z","timestamp":1726081761701},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030457143"},{"type":"electronic","value":"9783030457150"}],"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\/978-3-030-45715-0_1","type":"book-chapter","created":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T23:05:47Z","timestamp":1588115147000},"page":"1-12","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Feature Selection to Continuous Optimization"],"prefix":"10.1007","author":[{"given":"Hojjat","family":"Rakhshani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lhassane","family":"Idoumghar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julien","family":"Lepagnot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathieu","family":"Br\u00e9villiers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"unstructured":"Alom, M.Z., et al.: The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint \narXiv:1803.01164\n\n (2018)","key":"1_CR1"},{"unstructured":"Amos, B., Kolter, J.Z.: OptNet: differentiable optimization as a layer in neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 136\u2013145. JMLR. org (2017)","key":"1_CR2"},{"unstructured":"Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems, pp. 3981\u20133989 (2016)","key":"1_CR3"},{"unstructured":"Awad, N., Ali, M., Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical report (2016)","key":"1_CR4"},{"doi-asserted-by":"crossref","unstructured":"Brest, J., Mau\u010dec, M.S., Bo\u0161kovi\u0107, B.: iL-SHADE: improved L-SHADE algorithm for single objective real-parameter optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC). pp. 1188\u20131195. IEEE (2016)","key":"1_CR5","DOI":"10.1109\/CEC.2016.7743922"},{"doi-asserted-by":"crossref","unstructured":"Brest, J., Mau\u010dec, M.S., Bo\u0161kovi\u0107, B.: Single objective real-parameter optimization: algorithm JSO. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1311\u20131318. IEEE (2017)","key":"1_CR6","DOI":"10.1109\/CEC.2017.7969456"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"1_CR7","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR8","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. Signal Process. Mag. 29, 82\u201397 (2012)","journal-title":"Signal Process. Mag."},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.asoc.2018.02.037","volume":"66","author":"K Kang","year":"2018","unstructured":"Kang, K., Bae, C., Yeung, H.W.F., Chung, Y.Y.: A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization. Appl. Soft Comput. 66, 319\u2013329 (2018)","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"1_CR10","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1109\/31.1783","volume":"35","author":"MP Kennedy","year":"1988","unstructured":"Kennedy, M.P., Chua, L.O.: Neural networks for nonlinear programming. IEEE Trans. Circuits Syst. 35(5), 554\u2013562 (1988)","journal-title":"IEEE Trans. Circuits Syst."},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint \narXiv:1412.6980\n\n (2014)","key":"1_CR11"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)","key":"1_CR12"},{"unstructured":"Lei Ba, J., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint \narXiv:1607.06450\n\n (2016)","key":"1_CR13"},{"unstructured":"Li, K., Malik, J.: Learning to optimize. arXiv preprint \narXiv:1606.01885\n\n (2016)","key":"1_CR14"},{"doi-asserted-by":"crossref","unstructured":"Loshchilov, I.: CMA-ES with restarts for solving CEC 2013 benchmark problems. In: 2013 IEEE Congress on Evolutionary Computation, pp. 369\u2013376. IEEE (2013)","key":"1_CR15","DOI":"10.1109\/CEC.2013.6557593"},{"unstructured":"Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785\u20131791. IEEE (2005)","key":"1_CR16"},{"key":"1_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-12598-1_20","volume-title":"Evolutionary Multi-Criterion Optimization","author":"H Rakhshani","year":"2019","unstructured":"Rakhshani, H., Idoumghar, L., Lepagnot, J., Br\u00e9villiers, M.: MAC: many-objective automatic algorithm configuration. In: Deb, K., et al. (eds.) EMO 2019. LNCS, vol. 11411, pp. 241\u2013253. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-12598-1_20"},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1016\/j.asoc.2016.09.048","volume":"52","author":"H Rakhshani","year":"2017","unstructured":"Rakhshani, H., Rahati, A.: Snap-drift cuckoo search: a novel cuckoo search optimization algorithm. Appl. Soft Comput. 52, 771\u2013794 (2017)","journal-title":"Appl. Soft Comput."},{"unstructured":"Salimans, T., Kingma, D.P.: Weight normalization: a simple reparameterization to accelerate training of deep neural networks. In: Advances in Neural Information Processing Systems, pp. 901\u2013909 (2016)","key":"1_CR19"},{"unstructured":"Santurkar, S., Tsipras, D., Ilyas, A., Madry, A.: How does batch normalization help optimization? In: Advances in Neural Information Processing Systems, pp. 2483\u20132493 (2018)","key":"1_CR20"},{"issue":"5","key":"1_CR21","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1049\/ip-gtd:20045299","volume":"152","author":"T Senjyu","year":"2005","unstructured":"Senjyu, T., Saber, A., Miyagi, T., Shimabukuro, K., Urasaki, N., Funabashi, T.: Fast technique for unit commitment by genetic algorithm based on unit clustering. IEE Proc.-Gener. Transm. Distrib. 152(5), 705\u2013713 (2005)","journal-title":"IEE Proc.-Gener. Transm. Distrib."},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint \narXiv:1409.1556\n\n (2014)","key":"1_CR22"},{"unstructured":"Snoek, J., et al.: Scalable Bayesian optimization using deep neural networks. In: International Conference on Machine Learning, pp. 2171\u20132180 (2015)","key":"1_CR23"},{"issue":"4","key":"1_CR24","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Glob. Optim."},{"doi-asserted-by":"crossref","unstructured":"Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation, pp. 71\u201378. IEEE (2013)","key":"1_CR25","DOI":"10.1109\/CEC.2013.6557555"},{"doi-asserted-by":"crossref","unstructured":"Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658\u20131665. IEEE (2014)","key":"1_CR26","DOI":"10.1109\/CEC.2014.6900380"},{"issue":"5","key":"1_CR27","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","volume":"13","author":"J Zhang","year":"2009","unstructured":"Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945\u2013958 (2009)","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Artificial Evolution"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45715-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T23:12:48Z","timestamp":1588115568000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-45715-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030457143","9783030457150"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45715-0_1","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":"29 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Evolution (Evolution Artificielle)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mulhouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ae2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ea2019.inria.fr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}