{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:09:33Z","timestamp":1742994573234,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984121"},{"type":"electronic","value":"9789819984138"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8413-8_12","type":"book-chapter","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T01:02:10Z","timestamp":1708131730000},"page":"225-240","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation"],"prefix":"10.1007","author":[{"given":"Christian","family":"Haider","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabricio Olivetti","family":"de Franca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogdan","family":"Burlacu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Bachinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel","family":"Kronberger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Affenzeller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,18]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Asadzadeh, M.Z., G\u00e4nser, H.-P., M\u00fccke, M.: Symbolic regression based hybrid semiparametric modelling of processes: an example case of a bending process. Appl. Eng. Sci. 6, 100049 (2021)","DOI":"10.1016\/j.apples.2021.100049"},{"key":"12_CR2","unstructured":"Auguste, C., Malory, S., Smirnov, I.: A better method to enforce monotonic constraints in regression and classification trees (2020). arXiv:2011.00986"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Bachinger, F., Kronberger, G.: Comparing shape-constrained regression algorithms for data validation. In: Moreno-D\u00edaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) Computer Aided Systems Theory\u2014EUROCAST 2022, pp. 147\u2013154. Springer Nature Switzerland, Cham (2022)","DOI":"10.1007\/978-3-031-25312-6_17"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Baker, N., Alexander, F., Bremer, T., Hagberg, A., Kevrekidis, Y., Najm, H., Parashar, M., Patra, A., Sethian, J., Wild, S., et al.: Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence (2019)","DOI":"10.2172\/1478744"},{"issue":"337","key":"12_CR5","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1080\/01621459.1972.10481216","volume":"67","author":"RE Barlow","year":"1972","unstructured":"Barlow, R.E., Brunk, H.D.: The isotonic regression problem and its dual. J. Am. Stat. Assoc. 67(337), 140\u2013147 (1972)","journal-title":"J. Am. Stat. Assoc."},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Bladek, I., Krawiec, K.: Solving symbolic regression problems with formal constraints. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO \u201919, pp. 977\u2013984. Association for Computing Machinery, New York, NY, USA (2019)","DOI":"10.1145\/3321707.3321743"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Coello Coello, C.A.: Constraint-handling techniques used with evolutionary algorithms. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, GECCO \u201916 Companion, pp. 563\u2013587. Association for Computing Machinery, New York, NY, USA (2016)","DOI":"10.1145\/2908961.2926986"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Curmei, M., Hall, G.: Shape-constrained regression using sum of squares polynomials (2022)","DOI":"10.1287\/opre.2021.0383"},{"key":"12_CR9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ins.2018.02.040","volume":"442","author":"FO de Fran\u00e7a","year":"2018","unstructured":"de Fran\u00e7a, F.O.: A greedy search tree heuristic for symbolic regression. Inf. Sci. 442, 18\u201332 (2018)","journal-title":"Inf. Sci."},{"issue":"3","key":"12_CR10","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1162\/evco_a_00285","volume":"29","author":"FO de Franca","year":"2021","unstructured":"de Franca, F.O., Aldeia, G.S.I.: Interaction-transformation evolutionary algorithm for symbolic regression. Evol. Comput. 29(3), 367\u2013390 (2021)","journal-title":"Evol. Comput."},{"key":"12_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109855","volume":"132","author":"C Haider","year":"2023","unstructured":"Haider, C., de Franca, F., Burlacu, B., Kronberger, G.: Shape-constrained multi-objective genetic programming for symbolic regression. Appl. Soft Comput. 132, 109855 (2023)","journal-title":"Appl. Soft Comput."},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Haider, C., de\u00a0Fran\u00e7a, F.O., Kronberger, G., Burlacu, B.: Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO \u201922, pp. 938\u2013945. Association for Computing Machinery, New York, NY, USA (2022)","DOI":"10.1145\/3512290.3528714"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Haider, C., Kronberger, G.: Shape-constrained symbolic regression with NSGA-III. In: Moreno-D\u00edaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) Computer Aided Systems Theory\u2014EUROCAST 2022, pp. 164\u2013172. Springer Nature Switzerland, Cham (2022)","DOI":"10.1007\/978-3-031-25312-6_19"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Hickey, T., Ju, Q., Van\u00a0Emden, M.H.: Interval arithmetic: from principles to implementation. J. ACM 48(5), 1038\u20131068 (2001)","DOI":"10.1145\/502102.502106"},{"issue":"2","key":"12_CR15","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.ejor.2007.06.028","volume":"190","author":"SO Kimbrough","year":"2008","unstructured":"Kimbrough, S.O., Koehler, G.J., Lu, M., Wood, D.H.: On a feasible-infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. Eur. J. Oper. Res. 190(2), 310\u2013327 (2008)","journal-title":"Eur. J. Oper. Res."},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.ejor.2007.06.028","volume":"190","author":"SO Kimbrough","year":"2008","unstructured":"Kimbrough, S.O., Koehler, G.J., Lu, M.-C., Wood, D.H.: On a feasible-infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. Euopean J. Oper. Res. 190, 310\u2013327 (2008)","journal-title":"Euopean J. Oper. Res."},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Kronberger, G., de\u00a0Franca, F.O., Burlacu, B., Haider, C., Kommenda, M.: Shape-constrained symbolic regression-improving extrapolation with prior knowledge. Evol. Comput. 30(1), 75\u201398 (2022)","DOI":"10.1162\/evco_a_00294"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Kronberger, G., Kommenda, M., Promberger, A., Nickel, F.: Predicting friction system performance with symbolic regression and genetic programming with factor variables. In: Aguirre, H.E., Takadama, K. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, pp. 1278\u20131285. ACM (2018)","DOI":"10.1145\/3205455.3205522"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Kubal\u00edk, J., Derner, E., Babu\u0161ka, R.: Symbolic regression driven by training data and prior knowledge. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO \u201920, pp. 958\u2013966. Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3377930.3390152"},{"key":"12_CR20","unstructured":"Li, L., Fan, M., Singh, R., Riley, P.: Neural-guided symbolic regression with asymptotic constraints (2019). arXiv:1901.07714"},{"key":"12_CR21","volume-title":"Constrained Interval Arithmetic","author":"WA Lodwick","year":"1999","unstructured":"Lodwick, W.A.: Constrained Interval Arithmetic. Technical report, USA (1999)"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Muralidhar, N., Islam, M.R., Marwah, M., Karpatne, A., Ramakrishnan, N.: Incorporating prior domain knowledge into deep neural networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 36\u201345. IEEE (2018)","DOI":"10.1109\/BigData.2018.8621955"},{"issue":"2","key":"12_CR23","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1111\/j.0006-341X.2004.00184.x","volume":"60","author":"B Neelon","year":"2004","unstructured":"Neelon, B., Dunson, D.B.: Bayesian isotonic regression and trend analysis. Biom. 60(2), 398\u2013406 (2004)","journal-title":"Biom."},{"issue":"1","key":"12_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1080\/10618600.2012.707343","volume":"23","author":"D Papp","year":"2014","unstructured":"Papp, D., Alizadeh, F.: Shape-constrained estimation using nonnegative splines. J. Comput. Graph. Stat. 23(1), 211\u2013231 (2014)","journal-title":"J. Comput. Graph. Stat."},{"key":"12_CR25","unstructured":"Parrilo, P.A.: Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization. Ph.D. thesis, California Institute of Technology (2000)"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Piringer, D., Wagner, S., Haider, C., Fohler, A., Silber, S., Affenzeller, M.: Improving the flexibility of shape-constrained symbolic regression with extended constraints. In: Moreno-D\u00edaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) Computer Aided Systems Theory\u2014EUROCAST 2022, pp. 155\u2013163. Springer Nature Switzerland, Cham (2022)","DOI":"10.1007\/978-3-031-25312-6_18"},{"key":"12_CR27","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11747-019-00710-5","volume":"48","author":"A Rai","year":"2020","unstructured":"Rai, A.: Explainable AI: From black box to glass box. J. Acad. Mark. Sci. 48, 137\u2013141 (2020)","journal-title":"J. Acad. Mark. Sci."},{"key":"12_CR28","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys."},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Stewart, R., Ermon, S.: Label-free supervision of neural networks with physics and domain knowledge. Proc. AAAI Conf. Artif. Intell. 31(1), (2017)","DOI":"10.1609\/aaai.v31i1.10934"},{"issue":"1","key":"12_CR30","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1198\/TECH.2010.10111","volume":"53","author":"RJ Tibshirani","year":"2011","unstructured":"Tibshirani, R.J., Hoefling, H., Tibshirani, R.: Nearly-isotonic regression. Technometrics 53(1), 54\u201361 (2011)","journal-title":"Technometrics"},{"issue":"1","key":"12_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s43586-020-00001-2","volume":"1","author":"R van de Schoot","year":"2021","unstructured":"van de Schoot, R., Depaoli, S., King, R., Kramer, B., M\u00e4rtens, K., Tadesse, M.G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., et al.: Bayesian statistics and modelling. Nat. Rev. Methods Prim. 1(1), 1 (2021)","journal-title":"Nat. Rev. Methods Prim."}],"container-title":["Genetic and Evolutionary Computation","Genetic Programming Theory and Practice XX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8413-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T01:04:44Z","timestamp":1708131884000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8413-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819984121","9789819984138"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8413-8_12","relation":{},"ISSN":["1932-0167","1932-0175"],"issn-type":[{"type":"print","value":"1932-0167"},{"type":"electronic","value":"1932-0175"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"18 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}