{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T02:47:14Z","timestamp":1774406834697,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["301596\/2022-0"],"award-info":[{"award-number":["301596\/2022-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88887.802848\/2023-00"],"award-info":[{"award-number":["88887.802848\/2023-00"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/000408\/2025"],"award-info":[{"award-number":["UID\/000408\/2025"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,14]]},"DOI":"10.1145\/3712255.3734309","type":"proceedings-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T15:14:02Z","timestamp":1754925242000},"page":"2529-2538","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Call for Action: towards the next generation of symbolic regression benchmark"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0102-4958","authenticated-orcid":false,"given":"Guilherme Seidyo","family":"Imai Aldeia","sequence":"first","affiliation":[{"name":"Universidade Federal do ABC, Santo Andr\u00e9, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2254-8304","authenticated-orcid":false,"given":"Hengzhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Victoria University of Wellington, Wellington, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5540-6871","authenticated-orcid":false,"given":"Geoffrey","family":"Bomarito","sequence":"additional","affiliation":[{"name":"NASA Langley Research Center, Hampton, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6458-3423","authenticated-orcid":false,"given":"Miles","family":"Cranmer","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0879-4015","authenticated-orcid":false,"given":"Alcides","family":"Fonseca","sequence":"additional","affiliation":[{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa, Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8785-2959","authenticated-orcid":false,"given":"Bogdan","family":"Burlacu","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Upper Austria, Upper Austria, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1332-2960","authenticated-orcid":false,"given":"William G.","family":"La Cava","sequence":"additional","affiliation":[{"name":"Computational Health Informatics Program, Boston Children's Hospital, Boston, USA"},{"name":"Harvard Medical School, Boston, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2741-8736","authenticated-orcid":false,"given":"Fabr\u00edcio Olivetti","family":"de Fran\u00e7a","sequence":"additional","affiliation":[{"name":"Universidade Federal do ABC, Santo Andr\u00e9, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1352","article-title":"Evaluation and Benchmarking of Performance of Machine Learning and Symbolic Regression: Datasets, Software Tools and Prediction Models","volume":"7","author":"Abdalla Jamal A","year":"2024","unstructured":"Jamal A Abdalla, MZ Naser, Saleh M Alogla, Alireza Ghasemi, and Ahmad Naser. 2024. Evaluation and Benchmarking of Performance of Machine Learning and Symbolic Regression: Datasets, Software Tools and Prediction Models. ES General 7 (2024), 1352.","journal-title":"ES General"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2576768.2598291"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2023.3280250"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"945","author":"Biggio Luca","year":"2021","unstructured":"Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, and Giambattista Parascandolo. 2021. Neural Symbolic Regression that scales. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 936\u2013945. https:\/\/proceedings.mlr.press\/v139\/biggio21a.html"},{"key":"e_1_3_2_1_5_1","unstructured":"C. Bonnet. 1764. Contemplation de la nature. Number v. 2 in Contemplation de la nature. M.M. Rey. https:\/\/books.google.com\/books?id=Sm8GAAAAQAAJ"},{"key":"e_1_3_2_1_6_1","volume-title":"Chris Cave, Jaan Kasak, Valdemar Stentoft-Hansen, Victor Galindo Batanero, Tom Jelen, and Casper Wilstrup.","author":"Brol\u00f8s Kevin Ren\u00e9","year":"2021","unstructured":"Kevin Ren\u00e9 Brol\u00f8s, Meera Vieira Machado, Chris Cave, Jaan Kasak, Valdemar Stentoft-Hansen, Victor Galindo Batanero, Tom Jelen, and Casper Wilstrup. 2021. An Approach to Symbolic Regression Using Feyn. arXiv:2104.05417 [cs.LG] https:\/\/arxiv.org\/abs\/2104.05417"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1134\/s1064562422060230"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hke-JhA9Y7","author":"Cava William La","year":"2019","unstructured":"William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, and Jason Moore. 2019. Learning concise representations for regression by evolving networks of trees. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hke-JhA9Y7"},{"key":"e_1_3_2_1_9_1","volume-title":"Brush: An Interpretable Machine Learning Library. https:\/\/github.com\/cavalab\/brush\/tree\/multi_armed_bandits Accessed: 2025-03-29.","year":"2025","unstructured":"CavaLab. 2025. Brush: An Interpretable Machine Learning Library. https:\/\/github.com\/cavalab\/brush\/tree\/multi_armed_bandits Accessed: 2025-03-29."},{"key":"e_1_3_2_1_10_1","unstructured":"Miles Cranmer. 2023. Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. arXiv:2305.01582 [astro-ph.IM] https:\/\/arxiv.org\/abs\/2305.01582"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.02.040"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00285"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583131.3590346"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2024.3423681"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3512290.3528695"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597312"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3520304.3534040"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377930.3390237"},{"key":"e_1_3_2_1_19_1","volume-title":"Benchmarking optimization software with performance profiles. Mathematical programming 91","author":"Dolan Elizabeth D","year":"2002","unstructured":"Elizabeth D Dolan and Jorge J Mor\u00e9. 2002. Benchmarking optimization software with performance profiles. Mathematical programming 91 (2002), 201\u2013213."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3564719.3568697"},{"key":"e_1_3_2_1_21_1","unstructured":"R.P. Feynman R.B. Leighton and M.L. Sands. 2006. The Feynman Lectures on Physics. Number vol. 2 in The Feynman Lectures on Physics. Pearson\/Addison-Wesley. https:\/\/books.google.com.br\/books?id=AbruAAAAMAAJ"},{"key":"e_1_3_2_1_22_1","series-title":"The Feynman Lectures on Physics","volume-title":"The New Millennium Edition: Mainly Mechanics, Radiation, and Heat. Number","author":"Feynman R.P.","year":"2015","unstructured":"R.P. Feynman, R.B. Leighton, and M. Sands. 2015. The Feynman Lectures on Physics, Vol. I: The New Millennium Edition: Mainly Mechanics, Radiation, and Heat. Number vol. 1 in The Feynman Lectures on Physics. Basic Books. https:\/\/books.google.com.br\/books?id=d76DBQAAQBAJ"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_2_1_24_1","unstructured":"Sara Hooker. 2020. The Hardware Lottery. arXiv:2009.06489 [cs.CY] https:\/\/arxiv.org\/abs\/2009.06489"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2024.118821"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.15.3.168"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-29573-7_15"},{"key":"e_1_3_2_1_28_1","unstructured":"Ying Jin Weilin Fu Jian Kang Jiadong Guo and Jian Guo. 2020. Bayesian Symbolic Regression. arXiv:1910.08892 [stat.ME] https:\/\/arxiv.org\/abs\/1910.08892"},{"key":"e_1_3_2_1_29_1","unstructured":"Pierre-Alexandre Kamienny St\u00e9phane d'Ascoli Guillaume Lample and Francois Charton. 2022. End-to-end Symbolic Regression with Transformers. In Advances in Neural Information Processing Systems Alice H. Oh Alekh Agarwal Danielle Belgrave and Kyunghyun Cho (Eds.). https:\/\/openreview.net\/forum?id=GoOuIrDHG_Y"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-39958-0_5"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00743-2"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36599-0_7"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Johannes Kepler. 1619. Harmonices Mundi. Lincii Austri\u00e6.","DOI":"10.5479\/sil.135810.39088002800316"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-019-09371-3"},{"key":"e_1_3_2_1_35_1","volume-title":"Genetic Programming: On the Programming of Computers by Means of Natural Selection","author":"Koza John R.","year":"1992","unstructured":"John R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA."},{"key":"e_1_3_2_1_36_1","volume-title":"Genetic programming as a means for programming computers by natural selection. Statistics and computing 4","author":"Koza John R","year":"1994","unstructured":"John R Koza. 1994. Genetic programming as a means for programming computers by natural selection. Statistics and computing 4 (1994), 87\u2013112."},{"key":"e_1_3_2_1_37_1","volume-title":"Symbolic Regression","author":"Kronberger Gabriel","unstructured":"Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephan M. Winkler, and Michael Affenzeller. 2024. Symbolic Regression. Chapman & Hall \/ CRC Press."},{"key":"e_1_3_2_1_38_1","volume-title":"Ying Jin, and Jason H Moore.","author":"Cava William La","year":"2021","unstructured":"William La Cava, Bogdan Burlacu, Marco Virgolin, Michael Kommenda, Patryk Orzechowski, Fabr\u00edcio Olivetti de Fran\u00e7a, Ying Jin, and Jason H Moore. 2021. Contemporary symbolic regression methods and their relative performance. Advances in neural information processing systems 2021, DB1 (2021), 1."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.07.004"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00224"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00833-8"},{"key":"e_1_3_2_1_42_1","volume-title":"Oh (Eds.)","volume":"35","author":"Landajuela Mikel","year":"2022","unstructured":"Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Claudio P Santiago, Ignacio Aravena, Terrell Mundhenk, Garrett Mulcahy, and Brenden K Petersen. 2022. A Unified Framework for Deep Symbolic Regression. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 33985\u201333998. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/dbca58f35bddc6e4003b2dd80e42f838-Paper-Conference.pdf"},{"key":"e_1_3_2_1_43_1","volume-title":"Pickering","author":"Leavitt Henrietta S.","year":"1912","unstructured":"Henrietta S. Leavitt and Edward C. Pickering. 1912. Periods of 25 Variable Stars in the Small Magellanic Cloud. Harvard College Observatory Circular 173 (March 1912), 1\u20133."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108986"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10622-0"},{"key":"e_1_3_2_1_46_1","volume-title":"Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery. Journal of Data-centric Machine Learning Research","author":"Matsubara Yoshitomo","year":"2024","unstructured":"Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi, and Yoshitaka Ushiku. 2024. Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery. Journal of Data-centric Machine Learning Research (2024). https:\/\/openreview.net\/forum?id=qrUdrXsiXX"},{"key":"e_1_3_2_1_47_1","volume-title":"FFX: Fast, scalable, deterministic symbolic regression technology","author":"McConaghy Trent","year":"2011","unstructured":"Trent McConaghy. 2011. FFX: Fast, scalable, deterministic symbolic regression technology. In Genetic Programming Theory and Practice IX. Springer, 235\u2013260."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.103"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-017-0154-4"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3205455.3205539"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3520304.3534031"},{"key":"e_1_3_2_1_52_1","volume-title":"John T Gregg, Daniel J Goldberg, Praneel Chakraborty, Natasha L Ray, Daniel Himmelstein, Weixuan Fu, and Jason H Moore.","author":"Romano Joseph D","year":"2021","unstructured":"Joseph D Romano, Trang T Le, William La Cava, John T Gregg, Daniel J Goldberg, Praneel Chakraborty, Natasha L Ray, Daniel Himmelstein, Weixuan Fu, and Jason H Moore. 2021. PMLB v1.0: an open source dataset collection for benchmarking machine learning methods. arXiv preprint arXiv:2012.00058v2 (2021)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3638529.3654087"},{"key":"e_1_3_2_1_54_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"4450","author":"Sahoo Subham","year":"2018","unstructured":"Subham Sahoo, Christoph Lampert, and Georg Martius. 2018. Learning Equations for Extrapolation and Control. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, 4442\u20134450. https:\/\/proceedings.mlr.press\/v80\/sahoo18a.html"},{"key":"e_1_3_2_1_55_1","volume-title":"Distilling free-form natural laws from experimental data. science 324, 5923","author":"Schmidt Michael","year":"2009","unstructured":"Michael Schmidt and Hod Lipson. 2009. Distilling free-form natural laws from experimental data. science 324, 5923 (2009), 81\u201385."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-7747-2_8"},{"key":"e_1_3_2_1_57_1","volume-title":"Transformer-based Planning for Symbolic Regression. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=0rVXQEeFEL","author":"Shojaee Parshin","unstructured":"Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, and Chandan K. Reddy. 2023. Transformer-based Planning for Symbolic Regression. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=0rVXQEeFEL"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1088\/1475-7516\/2025\/01\/040"},{"key":"e_1_3_2_1_59_1","unstructured":"R. B. Tully and J. R. Fisher. 1977. A New Method of Determining Distance to Galaxies. Astronomy and Astrophysics 500 (Feb. 1977) 105\u2013117."},{"key":"e_1_3_2_1_60_1","volume-title":"AI Feynman: A physics-inspired method for symbolic regression. Science Advances 6, 16","author":"Udrescu Silviu-Marian","year":"2020","unstructured":"Silviu-Marian Udrescu and Max Tegmark. 2020. AI Feynman: A physics-inspired method for symbolic regression. Science Advances 6, 16 (2020), eaay2631."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00278"},{"key":"e_1_3_2_1_62_1","volume-title":"Pissis","author":"Virgolin Marco","year":"2022","unstructured":"Marco Virgolin and Solon P. Pissis. 2022. Symbolic Regression is NP-hard. arXiv:2207.01018 [cs.NE] https:\/\/arxiv.org\/abs\/2207.01018"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-020-09387-0"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2022.101061"}],"event":{"name":"GECCO '25 Companion: Genetic and Evolutionary Computation Conference Companion","location":"NH Malaga Hotel Malaga Spain","acronym":"GECCO '25 Companion","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"]},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3712255.3734309","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T11:44:47Z","timestamp":1759837487000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3712255.3734309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,14]]},"references-count":64,"alternative-id":["10.1145\/3712255.3734309","10.1145\/3712255"],"URL":"https:\/\/doi.org\/10.1145\/3712255.3734309","relation":{},"subject":[],"published":{"date-parts":[[2025,7,14]]},"assertion":[{"value":"2025-08-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}