{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:27:30Z","timestamp":1742945250363,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030862299"},{"type":"electronic","value":"9783030862305"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86230-5_19","type":"book-chapter","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:03:00Z","timestamp":1631005380000},"page":"239-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FERMAT: Feature Engineering with Grammatical Evolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8309-190X","authenticated-orcid":false,"given":"Mariana","family":"Monteiro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2154-0642","authenticated-orcid":false,"given":"Nuno","family":"Louren\u00e7o","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-6548","authenticated-orcid":false,"given":"Francisco B.","family":"Pereira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"issue":"4","key":"19_CR1","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s10710-007-9040-z","volume":"8","author":"F Archetti","year":"2007","unstructured":"Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genetic Program. Evolvable Mach. 8(4), 413\u2013432 (2007)","journal-title":"Genetic Program. Evolvable Mach."},{"key":"19_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/978-3-030-43722-0_34","volume-title":"Applications of Evolutionary Computation","author":"F Assun\u00e7\u00e3o","year":"2020","unstructured":"Assun\u00e7\u00e3o, F., Louren\u00e7o, N., Ribeiro, B., Machado, P.: Evolution of Scikit-learn pipelines with dynamic structured grammatical evolution. In: Castillo, P.A., Jim\u00e9nez Laredo, J.L., Fern\u00e1ndez de Vega, F. (eds.) EvoApplications 2020. LNCS, vol. 12104, pp. 530\u2013545. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43722-0_34"},{"issue":"8","key":"19_CR3","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR4","unstructured":"Castelli, M., Manzoni, L., Vanneschi, L.: An efficient genetic programming system with geometric semantic operators and its application to human oral bioavailability prediction. arXiv preprint arXiv:1208.2437 (2012)"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Dick, G., Rimoni, A.P., Whigham, P.A.: A re-examination of the use of genetic programming on the oral bioavailability problem. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1015\u20131022 (2015)","DOI":"10.1145\/2739480.2754771"},{"key":"19_CR6","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-3-030-05318-5_6","volume-title":"Automated Machine Learning","author":"M Feurer","year":"2019","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J.T., Blum, M., Hutter, F.: Auto-sklearn: efficient and robust automated machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 113\u2013134. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5_6"},{"key":"19_CR7","unstructured":"Foster, D., Karloff, H., Thaler, J.: Variable selection is hard. In: Conference on Learning Theory, pp. 696\u2013709. PMLR (2015)"},{"issue":"13\u201315","key":"19_CR8","doi-asserted-by":"publisher","first-page":"2824","DOI":"10.1016\/j.neucom.2008.09.024","volume":"72","author":"\u00c1B Jim\u00e9nez","year":"2009","unstructured":"Jim\u00e9nez, \u00c1.B., L\u00e1zaro, J.L., Dorronsoro, J.R.: Finding optimal model parameters by deterministic and annealed focused grid search. Neurocomputing 72(13\u201315), 2824\u20132832 (2009)","journal-title":"Neurocomputing"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Jolliffe, I.T.: Principal components in regression analysis. In: Principal component analysis, pp. 129\u2013155. Springer, New York (1986). https:\/\/doi.org\/10.1007\/978-1-4757-1904-8_8","DOI":"10.1007\/978-1-4757-1904-8_8"},{"key":"19_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-319-55696-3_6","volume-title":"Genetic Programming","author":"W La Cava","year":"2017","unstructured":"La Cava, W., Moore, J.: A general feature engineering wrapper for machine learning using $$\\epsilon $$-Lexicase survival. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., Garc\u00eda-S\u00e1nchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 80\u201395. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-55696-3_6"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-319-78717-6_6","volume-title":"Handbook of Grammatical Evolution","author":"N Louren\u00e7o","year":"2018","unstructured":"Louren\u00e7o, N., Assun\u00e7\u00e3o, F., Pereira, F.B., Costa, E., Machado, P.: Structured grammatical evolution: a dynamic approach. In: Ryan, C., O\u2019Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 137\u2013161. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-78717-6_6"},{"issue":"3","key":"19_CR12","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s10710-015-9262-4","volume":"17","author":"N Louren\u00e7o","year":"2016","unstructured":"Louren\u00e7o, N., Pereira, F.B., Costa, E.: Unveiling the properties of structured grammatical evolution. Genetic Program. Evolvable Mach. 17(3), 251\u2013289 (2016). https:\/\/doi.org\/10.1007\/s10710-015-9262-4","journal-title":"Genetic Program. Evolvable Mach."},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"McDermott, J., et al.: Genetic programming needs better benchmarks. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 791\u2013798 (2012)","DOI":"10.1145\/2330163.2330273"},{"key":"19_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1007\/978-3-540-24581-0_80","volume-title":"AI 2003: Advances in Artificial Intelligence","author":"MA Muharram","year":"2003","unstructured":"Muharram, M.A., Smith, G.D.: The effect of evolved attributes on classification algorithms. In: Gedeon, T.T.D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 933\u2013941. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-24581-0_80"},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-540-24650-3_36","volume-title":"Genetic Programming","author":"MA Muharram","year":"2004","unstructured":"Muharram, M.A., Smith, G.D.: Evolutionary feature construction using information gain and Gini index. In: Keijzer, M., O\u2019Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 379\u2013388. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24650-3_36"},{"key":"19_CR16","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-030-05318-5_8","volume-title":"Automated Machine Learning","author":"RS Olson","year":"2019","unstructured":"Olson, R.S., Moore, J.H.: TPOT: a tree-based pipeline optimization tool for automating machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 151\u2013160. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5_8"},{"issue":"1","key":"19_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1), 23\u201369 (2003)","journal-title":"Mach. Learn."},{"key":"19_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-319-55696-3_16","volume-title":"Genetic Programming","author":"AGC de S\u00e1","year":"2017","unstructured":"de S\u00e1, A.G.C., Pinto, W.J.G.S., Oliveira, L.O.V.B., Pappa, G.L.: RECIPE: a grammar-based framework for automatically evolving classification pipelines. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., Garc\u00eda-S\u00e1nchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 246\u2013261. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-55696-3_16"},{"issue":"1","key":"19_CR19","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2015","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148\u2013175 (2015)","journal-title":"Proc. IEEE"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 847\u2013855 (2013)","DOI":"10.1145\/2487575.2487629"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Vamathevan, J., et al.: Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18(1), 463\u2013477 (2019). https:\/\/doi.org\/10.1038\/s41573-019-0024-5","DOI":"10.1038\/s41573-019-0024-5"},{"key":"19_CR22","series-title":"Genetic and Evolutionary Computation","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-1-4939-0375-7_11","volume-title":"Genetic Programming Theory and Practice XI","author":"L Vanneschi","year":"2014","unstructured":"Vanneschi, L., Silva, S., Castelli, M., Manzoni, L.: Geometric semantic genetic programming for real life applications. In: Riolo, R., Moore, J.H., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XI. GEC, pp. 191\u2013209. Springer, New York (2014). https:\/\/doi.org\/10.1007\/978-1-4939-0375-7_11"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Whigham, P.A., et al.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: from Theory to Real-World Applications, vol. 16, pp. 33\u201341 (1995)","DOI":"10.1049\/cp:19951092"},{"issue":"1","key":"19_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10710-012-9177-2","volume":"14","author":"DR White","year":"2013","unstructured":"White, D.R., et al.: Better GP benchmarks: community survey results and proposals. Genetic Program. Evolvable Mach. 14(1), 3\u201329 (2013)","journal-title":"Genetic Program. Evolvable Mach."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86230-5_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T23:05:17Z","timestamp":1725750317000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86230-5_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030862299","9783030862305"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86230-5_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","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":"epia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.appia.pt\/epia2021\/","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":"108","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":"62","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":"57% - 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":"3.47","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":"1.36","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)"}}]}}