{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:33:50Z","timestamp":1783766030826,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Foundation for Science and Technology","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}]},{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["771416"],"award-info":[{"award-number":["771416"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["CF-2023-I-724"],"award-info":[{"award-number":["CF-2023-I-724"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation for Science and Technology, Portugal","award":["2021\/05322\/BD"],"award-info":[{"award-number":["2021\/05322\/BD"]}]},{"name":"Foundation for Science and Technology, Portugal","award":["UIDB\/00408\/2020"],"award-info":[{"award-number":["UIDB\/00408\/2020"]}]},{"DOI":"10.13039\/100012725","name":"Tecnol\u00f3gico Nacional de M\u00e9xico","doi-asserted-by":"publisher","award":["16788.23-P"],"award-info":[{"award-number":["16788.23-P"]}],"id":[{"id":"10.13039\/100012725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genet Program Evolvable Mach"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10710-024-09479-1","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T13:02:46Z","timestamp":1707397366000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Geometric semantic genetic programming with normalized and standardized random programs"],"prefix":"10.1007","volume":"25","author":[{"given":"Illya","family":"Bakurov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Manuel","family":"Mu\u00f1oz\u00a0Contreras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Castelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuno","family":"Rodrigues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Trujillo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Vanneschi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"9479_CR1","doi-asserted-by":"crossref","unstructured":"J. Albinati, G.l. Pappa, F.E. Otero et\u00a0al., The effect of distinct geometric semantic crossover operators in regression problems, in Genetic Programming: 18th European Conference, EuroGP 2015, Copenhagen, Denmark, April 8\u201310, 2015, Proceedings 18 (Springer, 2015), pp. 3\u201315","DOI":"10.1007\/978-3-319-16501-1_1"},{"key":"9479_CR2","doi-asserted-by":"crossref","unstructured":"I. Bakurov, L. Vanneschi, M. Castelli et al., Edda-v2\u2014An improvement of the evolutionary demes despeciation algorithm, in Parallel Problem Solving from Nature\u2014PPSN XV. ed. by A. Auger, C.M. Fonseca, N. Louren\u00e7o et al. (Springer International Publishing, Cham, 2018), pp. 185\u2013196","DOI":"10.1007\/978-3-319-99253-2_15"},{"key":"9479_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/app11114774","author":"I Bakurov","year":"2021","unstructured":"I. Bakurov, M. Buzzelli, M. Castelli et al., General purpose optimization library (GPOL): a flexible and efficient multi-purpose optimization library in python. Appl. Sci. (2021). https:\/\/doi.org\/10.3390\/app11114774","journal-title":"Appl. Sci."},{"key":"9479_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100913","volume":"65","author":"I Bakurov","year":"2021","unstructured":"I. Bakurov, M. Castelli, O. Gau et al., Genetic programming for stacked generalization. Swarm Evol. Comput. 65, 100913 (2021). https:\/\/doi.org\/10.1016\/j.swevo.2021.100913","journal-title":"Swarm Evol. Comput."},{"key":"9479_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.101028","volume":"69","author":"I Bakurov","year":"2022","unstructured":"I. Bakurov, M. Castelli, F. Fontanella et al., A novel binary classification approach based on geometric semantic genetic programming. Swarm Evol. Comput. 69, 101028 (2022). https:\/\/doi.org\/10.1016\/j.swevo.2021.101028","journal-title":"Swarm Evol. Comput."},{"key":"9479_CR6","doi-asserted-by":"crossref","unstructured":"L. Beadle, C.G. Johnson, Semantically driven crossover in genetic programming, in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (IEEE, 2008), pp. 111\u2013116","DOI":"10.1109\/CEC.2008.4630784"},{"key":"9479_CR7","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s10710-009-9082-5","volume":"10","author":"L Beadle","year":"2009","unstructured":"L. Beadle, C.G. Johnson, Semantic analysis of program initialisation in genetic programming. Genet. Program Evolvable Mach. 10, 307\u2013337 (2009)","journal-title":"Genet. Program Evolvable Mach."},{"key":"9479_CR8","doi-asserted-by":"crossref","unstructured":"L. Beadle, C.G. Johnson, Semantically driven mutation in genetic programming, in 2009 IEEE Congress on Evolutionary Computation (IEEE, 2009), pp. 1336\u20131342","DOI":"10.1109\/CEC.2009.4983099"},{"issue":"1","key":"9479_CR9","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10710-014-9218-0","volume":"16","author":"M Castelli","year":"2015","unstructured":"M. Castelli, S. Silva, L. Vanneschi, A C++ framework for geometric semantic genetic programming. Genet. Program Evolvable Mach. 16(1), 73\u201381 (2015)","journal-title":"Genet. Program Evolvable Mach."},{"key":"9479_CR10","doi-asserted-by":"publisher","unstructured":"M. Castelli, L. Trujillo, L. Vanneschi et\u00a0al., Geometric semantic genetic programming with local search, in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO \u201915 (Association for Computing Machinery, New York, NY, USA, 2015), pp. 999\u20131006. https:\/\/doi.org\/10.1145\/2739480.2754795","DOI":"10.1145\/2739480.2754795"},{"key":"9479_CR11","doi-asserted-by":"crossref","unstructured":"M. Castelli, L. Manzoni, I. Gon\u00e7alves, et\u00a0al., An analysis of geometric semantic crossover: a computational geometry approach, in International Joint Conference on Computational Intelligence (2016)","DOI":"10.5220\/0006056402010208"},{"key":"9479_CR12","unstructured":"F. Chollet et\u00a0al., Keras (2015). https:\/\/keras.io"},{"issue":"1","key":"9479_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"J. Derrac, S. Garc\u00eda, D. Molina et al., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3\u201318 (2011). https:\/\/doi.org\/10.1016\/j.swevo.2011.02.002","journal-title":"Swarm Evol. Comput."},{"key":"9479_CR14","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/978-3-319-16501-1_4","volume-title":"Genetic Programming","author":"I Gon\u00e7alves","year":"2015","unstructured":"I. Gon\u00e7alves, S. Silva, C.M. Fonseca, On the generalization ability of geometric semantic genetic programming, in Genetic Programming. ed. by P. Machado, M.I. Heywood, J. McDermott et al. (Springer, Cham, 2015), pp.41\u201352"},{"key":"9479_CR15","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-319-23485-4_28","volume-title":"Progress in Artificial Intelligence","author":"I Gon\u00e7alves","year":"2015","unstructured":"I. Gon\u00e7alves, S. Silva, C.M. Fonseca, Semantic learning machine: A feedforward neural network construction algorithm inspired by geometric semantic genetic programming, in Progress in Artificial Intelligence. ed. by F. Pereira, P. Machado, E. Costa et al. (Springer, Cham, 2015), pp.280\u2013285"},{"key":"9479_CR16","doi-asserted-by":"publisher","unstructured":"I. Gon\u00e7alves, S. Silva, C.M. Fonseca et\u00a0al., Unsure when to stop?, in Proceedings of the Genetic and Evolutionary Computation Conference (ACM, 2017). https:\/\/doi.org\/10.1145\/3071178.3071328","DOI":"10.1145\/3071178.3071328"},{"key":"9479_CR17","unstructured":"I. Gon\u00e7alves, An exploration of generalization and overfitting in genetic programming: standard and geometric semantic approaches. Ph.D. Thesis, Department of Informatics Engineering, University of Coimbra, Portugal., Coimbra, Portugal (2017), available at https:\/\/www.cisuc.uc.pt\/download-file\/13946\/sfxgEyeIRXv2dxxWgZS5"},{"key":"9479_CR18","volume-title":"Deep Learning","author":"IJ Goodfellow","year":"2016","unstructured":"I.J. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, 2016)"},{"key":"9479_CR19","doi-asserted-by":"publisher","unstructured":"K. He, Z. Zhang, S. Ren et\u00a0al., Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"9479_CR20","doi-asserted-by":"publisher","unstructured":"G. Huang, Z. Liu, L.V.D. Maaten et\u00a0al., Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE Computer Society, Los Alamitos, CA, USA, 2017), pp. 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243, https:\/\/doi.ieeecomputersociety.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"9479_CR21","unstructured":"S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. JMLR.org, ICML\u201915 (2015) pp. 448\u2013456"},{"key":"9479_CR22","volume-title":"Genetic Programming: On the Programming of Computers by Means of Natural Selection","author":"JR Koza","year":"1992","unstructured":"J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1 (MIT Press, Cambridge, 1992)"},{"key":"9479_CR23","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s10710-010-9112-3","volume":"11","author":"JR Koza","year":"2010","unstructured":"J.R. Koza, Human-competitive results produced by genetic programming. Genet. Program Evolvable Mach. 11, 251\u2013284 (2010)","journal-title":"Genet. Program Evolvable Mach."},{"key":"9479_CR24","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Efficient BackProp","author":"YA LeCun","year":"2012","unstructured":"Y.A. LeCun, L. Bottou, G.B. Orr et al., Efficient BackProp (Springer, Berlin, 2012), pp.9\u201348. https:\/\/doi.org\/10.1007\/978-3-642-35289-8_3"},{"key":"9479_CR25","doi-asserted-by":"crossref","unstructured":"J.F.B.S. Martins, L.O.V.B. Oliveira, L.F. Miranda et\u00a0al., Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming, in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO \u201918 (ACM, New York, NY, USA, 2018), pp. 1151\u20131158","DOI":"10.1145\/3205455.3205593"},{"key":"9479_CR26","doi-asserted-by":"crossref","unstructured":"J. McDermott, A. Agapitos, A. Brabazon et\u00a0al., Geometric semantic genetic programming for financial data, in Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23\u201325, 2014, Revised Selected Papers 17, (Springer, 2014), pp. 215\u2013226","DOI":"10.1007\/978-3-662-45523-4_18"},{"key":"9479_CR27","doi-asserted-by":"crossref","unstructured":"NF. McPhee, B. Ohs, T. Hutchison, Semantic building blocks in genetic programming, in Genetic Programming: 11th European Conference, EuroGP 2008, Naples, Italy, March 26\u201328, 2008. Proceedings 11 (Springer, 2008), pp. 134\u2013145","DOI":"10.1007\/978-3-540-78671-9_12"},{"key":"9479_CR28","doi-asserted-by":"crossref","unstructured":"A. Moraglio, K. Krawiec, C. Johnson, Geometric semantic genetic programming, in Parallel Problem Solving from Nature\u2014PPSN XII, ed. by C. Coello, V. Cutello, K. Deb, et al. Lecture Notes in Computer Science, vol. 7491 (Springer, Berlin, 2012), pp. 21\u201331","DOI":"10.1007\/978-3-642-32937-1_3"},{"key":"9479_CR29","unstructured":"V. Nair, G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, in Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML\u201910 (Omnipress, Madison, WI, USA, 2010), pp. 807\u2013814"},{"key":"9479_CR30","doi-asserted-by":"crossref","unstructured":"M. Nicolau, J. McDermott, Genetic programming symbolic regression: What is the prior on the prediction?, in Genetic Programming Theory and Practice XVII  (2020), pp. 201\u2013225","DOI":"10.1007\/978-3-030-39958-0_11"},{"key":"9479_CR31","doi-asserted-by":"crossref","unstructured":"L.O.V. Oliveira, F.E. Otero, G.L. Pappa, A dispersion operator for geometric semantic genetic programming, in Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2016 (2016), pp. 773\u2013780","DOI":"10.1145\/2908812.2908923"},{"key":"9479_CR32","unstructured":"I.\u00a0Ortigosa, J.G.R.\u00a0Lopez, A neural networks approach to residuary resistance of sailing yachts prediction, in Proceedings of the International Conference on Marine Engineering MARINE (2007), p. 250"},{"key":"9479_CR33","volume-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","author":"A Paszke","year":"2019","unstructured":"A. Paszke, S. Gross, F. Massa et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library (Curran Associates Inc., Red Hook, NY, USA, 2019)"},{"key":"9479_CR34","doi-asserted-by":"crossref","unstructured":"J.R. Quinlan, Combining instance-based and model-based learning, in Machine Learning, Proceedings of the Tenth International Conference, University of Massachusetts, Amherst, MA, USA, June 27\u201329, 1993 (1993), pp. 236\u2013243","DOI":"10.1016\/B978-1-55860-307-3.50037-X"},{"key":"9479_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2022.101085","volume":"18","author":"L Trujillo","year":"2022","unstructured":"L. Trujillo, J.M. Mu\u00f1oz Contreras, D.E. Hernandez et al., GSGP-CUDA\u2014a CUDA framework for geometric semantic genetic programming. SoftwareX 18, 101085 (2022). https:\/\/doi.org\/10.1016\/j.softx.2022.101085","journal-title":"SoftwareX"},{"key":"9479_CR36","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.enbuild.2012.03.003","volume":"49","author":"A Tsanas","year":"2012","unstructured":"A. Tsanas, A. Xifara, Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560\u2013567 (2012)","journal-title":"Energy Build."},{"key":"9479_CR37","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10710-010-9121-2","volume":"12","author":"NQ Uy","year":"2011","unstructured":"N.Q. Uy, N.X. Hoai, M. O\u2019Neill et al., Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program Evolvable Mach. 12, 91\u2013119 (2011)","journal-title":"Genet. Program Evolvable Mach."},{"key":"9479_CR38","doi-asserted-by":"crossref","unstructured":"L. Vanneschi, S. Silva, M. Castelli et\u00a0al., Geometric semantic genetic programming for real life applications, in Genetic Programming Theory and Practice XI (2014), pp. 191\u2013209","DOI":"10.1007\/978-1-4939-0375-7_11"},{"key":"9479_CR39","first-page":"191","volume-title":"Geometric Semantic Genetic Programming for Real Life Applications","author":"L Vanneschi","year":"2014","unstructured":"L. Vanneschi, S. Silva, M. Castelli et al., Geometric Semantic Genetic Programming for Real Life Applications (Springer, New York, 2014), pp.191\u2013209"},{"key":"9479_CR40","doi-asserted-by":"publisher","unstructured":"L. Vanneschi, I. Bakurov, M. Castelli, An initialization technique for geometric semantic GP based on demes evolution and despeciation, in 2017 IEEE Congress on Evolutionary Computation (CEC) (2017), pp. 113\u2013120. https:\/\/doi.org\/10.1109\/CEC.2017.7969303","DOI":"10.1109\/CEC.2017.7969303"},{"issue":"2","key":"9479_CR41","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TEVC.2008.926486","volume":"13","author":"EJ Vladislavleva","year":"2009","unstructured":"E.J. Vladislavleva, G.F. Smits, D. den Hertog, 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 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"12","key":"9479_CR42","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1016\/S0008-8846(98)00165-3","volume":"28","author":"IC Yeh","year":"1998","unstructured":"I.C. Yeh, Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797\u20131808 (1998)","journal-title":"Cem. Concr. Res."}],"container-title":["Genetic Programming and Evolvable Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-024-09479-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10710-024-09479-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-024-09479-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T08:18:42Z","timestamp":1716625122000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10710-024-09479-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,8]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9479"],"URL":"https:\/\/doi.org\/10.1007\/s10710-024-09479-1","relation":{},"ISSN":["1389-2576","1573-7632"],"issn-type":[{"value":"1389-2576","type":"print"},{"value":"1573-7632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,8]]},"assertion":[{"value":"31 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article. Several authors are board members of genetic programming and evolvable machines. Leonardo Trujillo, Sara Silva and Leonardo Vanneschi are Associate Editors, while Mauro Castelli is on the Editorial Board.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"6"}}