{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:59:22Z","timestamp":1777316362197,"version":"3.51.4"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032230041","type":"print"},{"value":"9783032230058","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-23005-8_12","type":"book-chapter","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:30:01Z","timestamp":1777314601000},"page":"189-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Extending Model Selection Criteria with Extrapolation and Sensitivity Penalties for Symbolic Regression"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5415-2534","authenticated-orcid":false,"given":"Fitria Wulandari","family":"Ramlan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0449-8224","authenticated-orcid":false,"given":"Colm","family":"O\u2019Riordan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1402-6995","authenticated-orcid":false,"given":"James","family":"McDermott","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"issue":"6","key":"12_CR1","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H Akaike","year":"2003","unstructured":"Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716\u2013723 (2003). https:\/\/doi.org\/10.1109\/TAC.1974.1100705","journal-title":"IEEE Trans. Autom. Control"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Cranmer, M.: PySR: fast & parallelized symbolic regression in Python\/Julia (2020). https:\/\/doi.org\/10.5281\/zenodo.4041459","DOI":"10.5281\/zenodo.4041459"},{"key":"12_CR3","doi-asserted-by":"publisher","unstructured":"Cranmer, M.: Interpretable machine learning for science with PySR and SymbolicRegression.jl. arXiv preprint arXiv:2305.01582 (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.01582","DOI":"10.48550\/arXiv.2305.01582"},{"key":"12_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2024.3423681","author":"FO de Franca","year":"2024","unstructured":"de Franca, F.O., et al.: SRBench++\u202f: principled benchmarking of symbolic regression with domain-expert interpretation. IEEE Trans. Evol. Comput. (2024). https:\/\/doi.org\/10.1109\/TEVC.2024.3423681","journal-title":"IEEE Trans. Evol. Comput."},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"de\u00a0Fran\u00e7a, F.O., et\u00a0al.: Interpretable symbolic regression for data science: analysis of the 2022 competition. arXiv preprint arXiv:2304.01117 (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.01117","DOI":"10.48550\/arXiv.2304.01117"},{"key":"12_CR6","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4643.001.0001","author":"PD Gr\u00fcnwald","year":"2007","unstructured":"Gr\u00fcnwald, P.D.: The Minimum Description Length Principle. The MIT Press (2007). https:\/\/doi.org\/10.7551\/mitpress\/4643.001.0001","journal-title":"The MIT Press"},{"issue":"454","key":"12_CR7","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1198\/016214501753168398","volume":"96","author":"MH Hansen","year":"2001","unstructured":"Hansen, M.H., Yu, B.: Model selection and the principle of minimum description length. J. Am. Stat. Assoc. 96(454), 746\u2013774 (2001). https:\/\/doi.org\/10.1198\/016214501753168398","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"12_CR8","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/BF00175355","volume":"4","author":"JR Koza","year":"1994","unstructured":"Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87\u2013112 (1994). https:\/\/doi.org\/10.1007\/BF00175355","journal-title":"Stat. Comput."},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Kronberger, G., Burlacu, B., Kommenda, M., Winkler, S.M., Affenzeller, M.: Symbolic Regression. CRC Press\/Taylor Francis (2024). https:\/\/doi.org\/10.1201\/9781315166407","DOI":"10.1201\/9781315166407"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Kronberger, G., de\u00a0Fran\u00e7a, F.O., Burlacu, B., Haider, C., Kommenda, M.: Shape-constrained symbolic regression\u2014improving extrapolation with prior knowledge. Evol. Comput. 30(1), 75\u201398 (2022). https:\/\/doi.org\/10.1162\/evco_a_00294","DOI":"10.1162\/evco_a_00294"},{"issue":"DB1","key":"12_CR11","first-page":"1","volume":"2021","author":"W La Cava","year":"2021","unstructured":"La Cava, W., et al.: Contemporary symbolic regression methods and their relative performance. Adv. Neural. Inf. Process. Syst. 2021(DB1), 1 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"Suppl 2","key":"12_CR12","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1007\/s10462-023-10591-4","volume":"56","author":"A Murari","year":"2023","unstructured":"Murari, A., Rossi, R., Spolladore, L., Lungaroni, M., Gaudio, P., Gelfusa, M.: A practical utility-based but objective approach to model selection for regression in scientific applications. Artif. Intell. Rev. 56(Suppl 2), 2825\u20132859 (2023). https:\/\/doi.org\/10.1007\/s10462-023-10591-4","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"12_CR13","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1002\/wics.199","volume":"4","author":"AA Neath","year":"2012","unstructured":"Neath, A.A., Cavanaugh, J.E.: The bayesian information criterion: background, derivation, and applications. Wiley Interdisc. Rev. Comput. Stat. 4(2), 199\u2013203 (2012). https:\/\/doi.org\/10.1002\/wics.199","journal-title":"Wiley Interdisc. Rev. Comput. Stat."},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Ramlan, F.W., Kronberger, G., O\u2019Riordan, C., McDermott, J.: Comparative analysis of model selection criteria for symbolic regression using genetic programming. In: Proceedings of the International Conference on Evolutionary Computation Theory and Applications (2025), accepted for publication","DOI":"10.1007\/978-3-032-15635-8_6"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Ramlan, F.W., O\u2019Riordan, C., Kronberger, G., McDermott, J.: Can synthetic data improve symbolic regression extrapolation performance? In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 2548\u20132555 (2025). https:\/\/doi.org\/10.1145\/3712255.3734356","DOI":"10.1145\/3712255.3734356"},{"issue":"5","key":"12_CR16","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465\u2013471 (1978). https:\/\/doi.org\/10.1016\/0005-1098(78)90005-5","journal-title":"Automatica"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81\u201385 (2009). https:\/\/doi.org\/10.1126\/science.1165893","DOI":"10.1126\/science.1165893"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Smits, G.F., Kotanchek, M.: Pareto-front exploitation in symbolic regression. In: Genetic Programming Theory and Practice II, pp. 283\u2013299. Springer (2005). https:\/\/doi.org\/10.1007\/0-387-23254-0_17","DOI":"10.1007\/0-387-23254-0_17"},{"issue":"7","key":"12_CR19","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1557\/MRS.2019.156","volume":"44","author":"S Sun","year":"2019","unstructured":"Sun, S., Ouyang, R., Zhang, B., Zhang, T.Y.: Data-driven discovery of formulas by symbolic regression. MRS Bull. 44(7), 559\u2013564 (2019). https:\/\/doi.org\/10.1557\/MRS.2019.156","journal-title":"MRS Bull."},{"issue":"2","key":"12_CR20","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). https:\/\/doi.org\/10.1007\/s10710-010-9121-2","journal-title":"Genet. Program Evolvable Mach."},{"key":"12_CR21","doi-asserted-by":"publisher","unstructured":"Prabhakaran, V., Ben\u00a0Hutchinson, M.M.: Perturbation sensitivity analysis to detect unintended model biases. In: Conference on Empirical Methods in Natural Language Processing (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1578","DOI":"10.18653\/v1\/D19-1578"}],"container-title":["Lecture Notes in Computer Science","Genetic Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23005-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:30:05Z","timestamp":1777314605000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23005-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032230041","9783032230058"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23005-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"28 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EuroGP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Genetic Programming (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurogp2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2026\/eurogp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}