{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:19:59Z","timestamp":1778620799856,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T00:00:00Z","timestamp":1720915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7,14]]},"DOI":"10.1145\/3638530.3664130","type":"proceedings-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T14:54:43Z","timestamp":1722524083000},"page":"2076-2082","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Interactive Symbolic Regression - A Study on Noise Sensitivity and Extrapolation Accuracy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9997-7083","authenticated-orcid":false,"given":"S. Sanjith","family":"Raghav","sequence":"first","affiliation":[{"name":"Amrita Vishwa Vidyapeetham, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5166-0840","authenticated-orcid":false,"given":"S. Tejesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Amrita Vishwa Vidyapeetham, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4487-6058","authenticated-orcid":false,"given":"Rishiikesh","family":"Balaji","sequence":"additional","affiliation":[{"name":"Amrita Vishwa Vidyapeetham, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0386-4639","authenticated-orcid":false,"given":"M.","family":"Sanjay","sequence":"additional","affiliation":[{"name":"Amrita Vishwa Vidyapeetham, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3762-4311","authenticated-orcid":false,"given":"C.","family":"Shunmuga","sequence":"additional","affiliation":[{"name":"Amrita Vishwa Vidyapeetham University, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Dynamic Mode Decomposition and Its Application in Various Domains: An Overview","author":"Akshay S.","unstructured":"S. Akshay, K. P. Soman, Neethu Mohan, and S. Sachin Kumar. 2021. Dynamic Mode Decomposition and Its Application in Various Domains: An Overview. Springer International Publishing, Cham, 121--132."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377929.3398099"},{"key":"e_1_3_2_1_3_1","unstructured":"SRBench Competition. 2022. Retrieved April 06 2024 from https:\/\/cavalab.org\/srbench\/competition-2022\/"},{"key":"e_1_3_2_1_4_1","volume-title":"Zenodo","author":"Cranmer Miles","year":"2020","unstructured":"Miles Cranmer. 2020. Pysr: Fast & parallelized symbolic regression in python\/julia. Zenodo, September (2020)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Laure Crochepierre Lydia Boudjeloud-Assala and Vincent Barbesant. 2022. Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks.. In IJCAI. 5900--5903.","DOI":"10.24963\/ijcai.2022\/849"},{"key":"e_1_3_2_1_6_1","volume-title":"A reinforcement learning approach to domain-knowledge inclusion using grammar guided symbolic regression. arXiv preprint arXiv:2202.04367","author":"Crochepierre Laure","year":"2022","unstructured":"Laure Crochepierre, Lydia Boudjeloud-Assala, and Vincent Barbesant. 2022. A reinforcement learning approach to domain-knowledge inclusion using grammar guided symbolic regression. arXiv preprint arXiv:2202.04367 (2022)."},{"key":"e_1_3_2_1_7_1","unstructured":"Fabr\u00edcio Olivetti de Fran\u00e7a Marco Virgolin Michael Kommenda MS Majumder M Cranmer Guilherme Espada Leon Ingelse Alcides Fonseca Mikel Landajuela B Petersen et al. 2023. Interpretable symbolic regression for data science: Analysis of the 2022 competition. arXiv preprint arXiv:2304.01117 (2023)."},{"key":"e_1_3_2_1_8_1","unstructured":"SRBench A Living Benchmark for Symbolic Regression. 2024. Retrieved April 06 2024 from https:\/\/cavalab.org\/srbench\/"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCIS-ISIS.2016.0187"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","unstructured":"Makoto Fukumoto and Kota Nomura. 2018. A proposal for distributed interactive differential evolution: in a case of creating sign sounds for multiple users. 125--126. 10.1145\/3205651.3205690","DOI":"10.1145\/3205651.3205690"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Pradhumna Guruprasad Shreyas Nagesh Aditya N Sampath G Jeyakumar et al. 2023. Assessing the Efficacy of Symbolic Regression for Scientific Data Classification and Regression. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE 1--6.","DOI":"10.1109\/ICCCNT56998.2023.10308129"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3512290.3528757"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Isha Indhu S. Kavya S. Kumar U. Vamsi Krishna Neethu Mohan V. Sowmya and K.P. Soman. 2020. Investigating the Significance of Dynamic Mode Decomposition for Fast and Accurate Parameter Estimation in Power Grids. In 2020 11th International Conference on Computing Communication and Networking Technologies (ICCCNT). 1--5.","DOI":"10.1109\/ICCCNT49239.2020.9225579"},{"key":"e_1_3_2_1_14_1","volume-title":"Bayesian symbolic regression. arXiv preprint arXiv:1910.08892","author":"Jin Ying","year":"2019","unstructured":"Ying Jin, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo. 2019. Bayesian symbolic regression. arXiv preprint arXiv:1910.08892 (2019)."},{"key":"e_1_3_2_1_15_1","first-page":"10269","article-title":"End-to-end symbolic regression with transformers","volume":"35","author":"Kamienny Pierre-Alexandre","year":"2022","unstructured":"Pierre-Alexandre Kamienny, St\u00e9phane d'Ascoli, Guillaume Lample, and Fran\u00e7ois Charton. 2022. End-to-end symbolic regression with transformers. Advances in Neural Information Processing Systems 35 (2022), 10269--10281.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 5261--5263","author":"Kim Joanne T","year":"2021","unstructured":"Joanne T Kim, Sookyung Kim, and Brenden K Petersen. 2021. An interactive visualization platform for deep symbolic regression. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 5261--5263."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"J Nathan Kutz Steven L Brunton Bingni W Brunton and Joshua L Proctor. 2016. Dynamic mode decomposition: data-driven modeling of complex systems. SIAM.","DOI":"10.1137\/1.9781611974508"},{"key":"e_1_3_2_1_18_1","volume-title":"Marco Virgolin, Ying Jin, Michael Kommenda, and Jason H Moore.","author":"Cava William La","year":"2021","unstructured":"William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabr\u00edcio Olivetti de Fran\u00e7a, Marco Virgolin, Ying Jin, Michael Kommenda, and Jason H Moore. 2021. Contemporary symbolic regression methods and their relative performance. arXiv preprint arXiv:2107.14351 (2021)."},{"key":"e_1_3_2_1_19_1","volume-title":"FFX: Fast, scalable, deterministic symbolic regression technology. Genetic Programming Theory and Practice IX","author":"McConaghy Trent","year":"2011","unstructured":"Trent McConaghy. 2011. FFX: Fast, scalable, deterministic symbolic regression technology. Genetic Programming Theory and Practice IX (2011), 235--260."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3205455.3205539"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583133.3596301"},{"key":"e_1_3_2_1_22_1","volume-title":"Research progress survey on interactive evolutionary computation. Journal of Ambient Intelligence and Humanized Computing","author":"Pei Yan","year":"2018","unstructured":"Yan Pei and Hideyuki Takagi. 2018. Research progress survey on interactive evolutionary computation. Journal of Ambient Intelligence and Humanized Computing (2018), 1--14."},{"key":"e_1_3_2_1_23_1","volume-title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. arXiv preprint arXiv:1912.04871","author":"Petersen Brenden K","year":"2019","unstructured":"Brenden K Petersen, Mikel Landajuela, T Nathan Mundhenk, Claudio P Santiago, Soo K Kim, and Joanne T Kim. 2019. Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. arXiv preprint arXiv:1912.04871 (2019)."},{"key":"e_1_3_2_1_24_1","volume-title":"Aadharsh Aadhithya A, Neethu Mohan, Sachin Kumar S, and Soman K P.","author":"Ravula Pinninti Sai","year":"2023","unstructured":"Pinninti Sai Ravula, Hema Radhika Reddy, Aadharsh Aadhithya A, Neethu Mohan, Sachin Kumar S, and Soman K P. 2023. Higher Order Dynamic Mode Decomposition for Robust Parameter Estimation in Power Grids. In 2023 3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). 1--6."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319619.3326871"},{"key":"e_1_3_2_1_26_1","unstructured":"Trevor Stephens. 2022. Retrieved April 06 2024 from https:\/\/github.com\/trevorstephens\/gplearn"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.949485"},{"key":"e_1_3_2_1_28_1","volume-title":"Benchmarking state-of-the-art symbolic regression algorithms. Genetic programming and evolvable machines 22, 1","author":"\u017degklitz Jan","year":"2021","unstructured":"Jan \u017degklitz and Petr Po\u0161\u00edk. 2021. Benchmarking state-of-the-art symbolic regression algorithms. Genetic programming and evolvable machines 22, 1 (2021), 5--33."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2022.101061"}],"event":{"name":"GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion","location":"Melbourne VIC Australia","acronym":"GECCO '24 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\/10.1145\/3638530.3664130","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638530.3664130","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:12Z","timestamp":1750291572000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638530.3664130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,14]]},"references-count":29,"alternative-id":["10.1145\/3638530.3664130","10.1145\/3638530"],"URL":"https:\/\/doi.org\/10.1145\/3638530.3664130","relation":{},"subject":[],"published":{"date-parts":[[2024,7,14]]},"assertion":[{"value":"2024-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}