{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:36:18Z","timestamp":1743014178939,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030917012"},{"type":"electronic","value":"9783030917029"}],"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\/978-3-030-91702-9_16","type":"book-chapter","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T20:02:46Z","timestamp":1638043366000},"page":"234-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator"],"prefix":"10.1007","author":[{"given":"Gustavo F. V.","family":"de Oliveira","sequence":"first","affiliation":[]},{"given":"Marcus H. S.","family":"Mendes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","unstructured":"Castelli, M., Manzoni, L., Mariot, L., Saletta, M.: Extending local search in geometric semantic genetic programming, pp. 775\u2013787, August 2019. https:\/\/doi.org\/10.1007\/978-3-030-30241-2_64","DOI":"10.1007\/978-3-030-30241-2_64"},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.1016\/j.neucom.2017.10.047","volume":"275","author":"C Chen","year":"2018","unstructured":"Chen, C., Luo, C., Jiang, Z.: Block building programming for symbolic regression. Neurocomputing 275, 1973\u20131980 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2017.10.047","journal-title":"Neurocomputing"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/09-SS051","volume":"4","author":"MP Fay","year":"2010","unstructured":"Fay, M.P., Proschan, M.A.: Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat. Surv. 4, 1 (2010)","journal-title":"Stat. Surv."},{"key":"16_CR4","first-page":"2171","volume":"13","author":"FA Fortin","year":"2012","unstructured":"Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagn\u00e9, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171\u20132175 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.asoc.2017.06.050","volume":"60","author":"MA Haeri","year":"2017","unstructured":"Haeri, M.A., Ebadzadeh, M.M., Folino, G.: Statistical genetic programming for symbolic regression. Appl. Soft Comput. 60, 447\u2013469 (2017)","journal-title":"Appl. Soft Comput."},{"key":"16_CR6","doi-asserted-by":"publisher","unstructured":"Hagiwara, M.: Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. In: Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), vol. 1, pp. 713\u2013716 (1993). https:\/\/doi.org\/10.1109\/IJCNN.1993.714013","DOI":"10.1109\/IJCNN.1993.714013"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Hooper, D.C., Flann, N.S.: Improving the accuracy and robustness of genetic programming through expression simplification. In: Proceedings of the 1st Annual Conference on Genetic Programming, p. 428. MIT Press, Cambridge (1996)","DOI":"10.7551\/mitpress\/3242.003.0072"},{"key":"16_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/3-540-36599-0_7","volume-title":"Genetic Programming","author":"M Keijzer","year":"2003","unstructured":"Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 70\u201382. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-36599-0_7"},{"key":"16_CR9","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":"16_CR10","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":"16_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-642-02481-8_24","volume-title":"Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living","author":"M Naoki","year":"2009","unstructured":"Naoki, M., McKay, B., Xuan, N., Daryl, E., Takeuchi, S.: A new method for simplifying algebraic expressions in genetic programming called equivalent decision simplification. In: Omatu, S., Rocha, M.P., Bravo, J., Fern\u00e1ndez, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 171\u2013178. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02481-8_24"},{"key":"16_CR12","unstructured":"Moritz, P., et al.: Ray: a distributed framework for emerging AI applications. CoRR (2017). http:\/\/arxiv.org\/abs\/1712.05889"},{"key":"16_CR13","series-title":"Natural Computing Series","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/978-3-642-33206-7_9","volume-title":"Theory and Principled Methods for the Design of Metaheuristics","author":"R Poli","year":"2014","unstructured":"Poli, R., McPhee, N.F.: Parsimony pressure made easy: solving the problem of bloat in GP. In: Borenstein, Y., Moraglio, A. (eds.) Theory and Principled Methods for the Design of Metaheuristics. NCS, pp. 181\u2013204. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-642-33206-7_9"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s10710-011-9150-5","volume":"13","author":"S Silva","year":"2012","unstructured":"Silva, S., Dignum, S., Vanneschi, L.: Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genetic Program. Evol. Mach. 13, 197\u2013238 (2012). https:\/\/doi.org\/10.1007\/s10710-011-9150-5","journal-title":"Genetic Program. Evol. Mach."},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Sivanandam, S., Deepa, S.: Genetic Programming, pp. 131\u2013163. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-73190-0_6","DOI":"10.1007\/978-3-540-73190-0_6"},{"key":"16_CR16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ins.2015.11.010","volume":"333","author":"L Trujillo","year":"2015","unstructured":"Trujillo, L., Mu\u00f1oz, L., Galv\u00e1n-L\u00f3pez, E., Silva, S.: Neat genetic programming: controlling bloat naturally. Inf. Sci. 333, 21\u201343 (2015). https:\/\/doi.org\/10.1016\/j.ins.2015.11.010","journal-title":"Inf. Sci."},{"key":"16_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-642-12148-7_16","volume-title":"Genetic Programming","author":"NQ Uy","year":"2010","unstructured":"Uy, N.Q., Hien, N.T., Hoai, N.X., O\u2019Neill, M.: Improving the generalisation ability of genetic programming with semantic similarity based crossover. In: Esparcia-Alc\u00e1zar, A.I., Ek\u00e1rt, A., Silva, S., Dignum, S., Uyar, A.\u015e (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 184\u2013195. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12148-7_16"},{"issue":"2","key":"16_CR18","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10710-010-9121-2","volume":"12","author":"NQ Uy","year":"2011","unstructured":"Uy, N.Q., Hoai, N.X., O\u2019Neill, M., McKay, R.I., Galv\u00e1n-L\u00f3pez, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program Evolvable Mach. 12(2), 91\u2013119 (2011)","journal-title":"Genet. Program Evolvable Mach."},{"issue":"2","key":"16_CR19","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TEVC.2008.926486","volume":"13","author":"EJ Vladislavleva","year":"2008","unstructured":"Vladislavleva, E.J., Smits, G.F., Den Hertog, D.: Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Trans. Evol. Comput. 13(2), 333\u2013349 (2008)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"16_CR20","doi-asserted-by":"publisher","unstructured":"Wong, P., Zhang, M.: Algebraic simplification of gp programs during evolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 927\u2013934, GECCO 2006. Association for Computing Machinery, New York, NY, USA (2006). https:\/\/doi.org\/10.1145\/1143997.1144156","DOI":"10.1145\/1143997.1144156"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91702-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:34:30Z","timestamp":1726202070000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91702-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030917012","9783030917029"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91702-9_16","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":"28 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","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":"29 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/c4ai.inova.usp.br\/bracis\/","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":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"192","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":"77","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":"40% - 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":"3.1","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":"Due to COVID-19, the conference was held as an online event.","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)"}}]}}