{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T22:42:09Z","timestamp":1787611329592,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":50,"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:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF2310100"],"award-info":[{"award-number":["W911NF2310100"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7,14]]},"DOI":"10.1145\/3638529.3654030","type":"proceedings-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T12:33:04Z","timestamp":1720441984000},"page":"277-285","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1469-3813","authenticated-orcid":false,"given":"Amanda","family":"Bertschinger","sequence":"first","affiliation":[{"name":"Department of Computer Science \/ Morphology, Evolution, & Cognition Laboratory, University of Vermont, South Burlington, Vermont, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4614-0792","authenticated-orcid":false,"given":"James","family":"Bagrow","sequence":"additional","affiliation":[{"name":"Department of Mathematics & Statistics and Vermont Complex Systems Center, University of Vermont, South Burlington, Vermont, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8515-0822","authenticated-orcid":false,"given":"Joshua","family":"Bongard","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Vermont Complex Systems Center \/ Morphology, Evolution, & Cognition Laboratory, University of Vermont, South Burlington, Vermont, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2576768.2598291"},{"key":"e_1_3_2_1_2_1","volume-title":"Controllable Neural Symbolic Regression. arXiv preprint arXiv:2304.10336","author":"Bendinelli Tommaso","year":"2023","unstructured":"Tommaso Bendinelli, Luca Biggio, and Pierre-Alexandre Kamienny. 2023. Controllable Neural Symbolic Regression. arXiv preprint arXiv:2304.10336 (2023)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43421-1_10"},{"key":"e_1_3_2_1_4_1","unstructured":"Luca Biggio Tommaso Bendinelli Aurelien Lucchi and Giambattista Parascandolo. 2020. A seq2seq approach to symbolic regression. In Learning Meets Combinatorial Algorithms at NeurIPS2020."},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 936--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 International Conference on Machine Learning. PMLR, 936--945."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC45853.2021.9504792"},{"key":"e_1_3_2_1_7_1","article-title":"Symbolic regression via neural networks. Chaos","volume":"33","author":"Boddupalli N","year":"2023","unstructured":"N Boddupalli, T Matchen, and J Moehlis. 2023. Symbolic regression via neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science 33, 8 (2023).","journal-title":"An Interdisciplinary Journal of Nonlinear Science"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2012.2863"},{"key":"e_1_3_2_1_9_1","volume-title":"Fast neural models for symbolic regression at scale. arXiv preprint arXiv:2007.10784","author":"Costa Allan","year":"2020","unstructured":"Allan Costa, Rumen Dangovski, Owen Dugan, Samuel Kim, Pawan Goyal, Marin Solja\u010di\u0107, and Joseph Jacobson. 2020. Fast neural models for symbolic regression at scale. arXiv preprint arXiv:2007.10784 (2020)."},{"key":"e_1_3_2_1_10_1","volume-title":"Interpretable machine learning for science with PySR and SymbolicRegression. jl. arXiv preprint arXiv:2305.01582","author":"Cranmer Miles","year":"2023","unstructured":"Miles Cranmer. 2023. Interpretable machine learning for science with PySR and SymbolicRegression. jl. arXiv preprint arXiv:2305.01582 (2023)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9270-6"},{"key":"e_1_3_2_1_12_1","volume-title":"Neural modularity helps organisms evolve to learn new skills without forgetting old skills. PLoS computational biology 11, 4","author":"Ellefsen Kai Olav","year":"2015","unstructured":"Kai Olav Ellefsen, Jean-Baptiste Mouret, and Jeff Clune. 2015. Neural modularity helps organisms evolve to learn new skills without forgetting old skills. PLoS computational biology 11, 4 (2015), e1004128."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14020596"},{"key":"e_1_3_2_1_15_1","volume-title":"Neuroevolution: from architectures to learning. Evolutionary intelligence 1","author":"Floreano Dario","year":"2008","unstructured":"Dario Floreano, Peter D\u00fcrr, and Claudio Mattiussi. 2008. Neuroevolution: from architectures to learning. Evolutionary intelligence 1 (2008), 47--62."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3067574"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109855"},{"key":"e_1_3_2_1_18_1","volume-title":"Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2009.933094"},{"key":"e_1_3_2_1_20_1","volume-title":"End-to-end symbolic regression with transformers. arXiv preprint arXiv:2204.10532","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. arXiv preprint arXiv:2204.10532 (2022)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36599-0_7"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:GENP.0000030195.77571.f9"},{"key":"e_1_3_2_1_23_1","volume-title":"Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858","author":"Keskar Nitish Shirish","year":"2019","unstructured":"Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019. Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858 (2019)."},{"key":"e_1_3_2_1_24_1","volume-title":"Integration of neural network-based symbolic regression in deep learning for scientific discovery","author":"Kim Samuel","year":"2020","unstructured":"Samuel Kim, Peter Y Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir \u010ceperi\u0107, and Marin Solja\u010di\u0107. 2020. Integration of neural network-based symbolic regression in deep learning for scientific discovery. IEEE transactions on neural networks and learning systems 32, 9 (2020), 4166--4177."},{"key":"e_1_3_2_1_25_1","volume-title":"Genetic Programming: On the Programming of Computers by Means of Natural Selection. Bradford. https:\/\/books.google.com\/books?id=Bhtxo60BV0EC","author":"Koza J.R.","year":"1992","unstructured":"J.R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Bradford. https:\/\/books.google.com\/books?id=Bhtxo60BV0EC"},{"key":"e_1_3_2_1_26_1","volume-title":"Genetic programming II: automatic discovery of reusable programs","author":"Koza John R","unstructured":"John R Koza. 1994. Genetic programming II: automatic discovery of reusable programs. MIT press."},{"key":"e_1_3_2_1_27_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_28_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Li Wenqiang","year":"2022","unstructured":"Wenqiang Li, Weijun Li, Linjun Sun, Min Wu, Lina Yu, Jingyi Liu, Yanjie Li, and Songsong Tian. 2022. Transformer-based model for symbolic regression via joint supervised learning. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_29_1","volume-title":"Extrapolation and learning equations. arXiv preprint arXiv:1610.02995","author":"Martius Georg","year":"2016","unstructured":"Georg Martius and Christoph H Lampert. 2016. Extrapolation and learning equations. arXiv preprint arXiv:1610.02995 (2016)."},{"key":"e_1_3_2_1_30_1","first-page":"762","article-title":"Training feedforward neural networks using genetic algorithms","volume":"89","author":"Montana David J","year":"1989","unstructured":"David J Montana, Lawrence Davis, et al. 1989. Training feedforward neural networks using genetic algorithms.. In IJCAI, Vol. 89. 762--767.","journal-title":"IJCAI"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1096\/fasebj.1.5.3315805"},{"key":"e_1_3_2_1_32_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_33_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 652--660","author":"Qi Charles R","year":"2017","unstructured":"Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 652--660."},{"key":"e_1_3_2_1_34_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.05.023"},{"key":"e_1_3_2_1_36_1","volume-title":"International Conference on Machine Learning. PMLR, 4442--4450","author":"Sahoo Subham","year":"2018","unstructured":"Subham Sahoo, Christoph Lampert, and Georg Martius. 2018. Learning equations for extrapolation and control. In International Conference on Machine Learning. PMLR, 4442--4450."},{"key":"e_1_3_2_1_37_1","volume-title":"Genetic programming theory and practice","author":"Schmidt Michael","unstructured":"Michael Schmidt and Hod Lipson. 2009. Symbolic regression of implicit equations. In Genetic programming theory and practice VII. Springer, 73--85."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2008.919006"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-018-0006-z"},{"key":"e_1_3_2_1_40_1","volume-title":"Evolving neural networks through augmenting topologies. Evolutionary computation 10, 2","author":"Stanley Kenneth O","year":"2002","unstructured":"Kenneth O Stanley and Risto Miikkulainen. 2002. Evolving neural networks through augmenting topologies. Evolutionary computation 10, 2 (2002), 99--127."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjc\/s10052-021-08950-y"},{"key":"e_1_3_2_1_42_1","volume-title":"SymbolicGPT: A generative transformer model for symbolic regression. arXiv preprint arXiv:2106.14131","author":"Valipour Mojtaba","year":"2021","unstructured":"Mojtaba Valipour, Bowen You, Maysum Panju, and Ali Ghodsi. 2021. SymbolicGPT: A generative transformer model for symbolic regression. arXiv preprint arXiv:2106.14131 (2021)."},{"key":"e_1_3_2_1_43_1","volume-title":"SymFormer: End-to-end symbolic regression using transformer-based architecture. arXiv preprint arXiv:2205.15764","author":"Vastl Martin","year":"2022","unstructured":"Martin Vastl, Jon\u00e1\u0161 Kulh\u00e1nek, Jir\u00ed Kubal\u00edk, Erik Derner, and Robert Babu\u0161ka. 2022. SymFormer: End-to-end symbolic regression using transformer-based architecture. arXiv preprint arXiv:2205.15764 (2022)."},{"key":"e_1_3_2_1_44_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_45_1","volume-title":"Improving model-based genetic programming for symbolic regression of small expressions. Evolutionary computation 29, 2","author":"Virgolin Marco","year":"2021","unstructured":"Marco Virgolin, Tanja Alderliesten, Cees Witteveen, and Peter AN Bosman. 2021. Improving model-based genetic programming for symbolic regression of small expressions. Evolutionary computation 29, 2 (2021), 211--237."},{"key":"e_1_3_2_1_46_1","volume-title":"Language models with transformers. arXiv preprint arXiv:1904.09408","author":"Wang Chenguang","year":"2019","unstructured":"Chenguang Wang, Mu Li, and Alexander J Smola. 2019. Language models with transformers. arXiv preprint arXiv:1904.09408 (2019)."},{"key":"e_1_3_2_1_47_1","volume-title":"Applied linear regression","author":"Weisberg Sanford","unstructured":"Sanford Weisberg. 2005. Applied linear regression. Vol. 528. John Wiley & Sons."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-27250-9_13"},{"key":"e_1_3_2_1_49_1","volume-title":"Deep learning and symbolic regression for discovering parametric equations","author":"Zhang Michael","year":"2023","unstructured":"Michael Zhang, Samuel Kim, Peter Y Lu, and Marin Solja\u010di\u0107. 2023. Deep learning and symbolic regression for discovering parametric equations. IEEE Transactions on Neural Networks and Learning Systems (2023)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108825"}],"event":{"name":"GECCO '24: Genetic and Evolutionary Computation Conference","location":"Melbourne VIC Australia","acronym":"GECCO '24","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"]},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638529.3654030","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3638529.3654030","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638529.3654030","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638529.3654030","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:56:50Z","timestamp":1750276610000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638529.3654030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,14]]},"references-count":50,"alternative-id":["10.1145\/3638529.3654030","10.1145\/3638529"],"URL":"https:\/\/doi.org\/10.1145\/3638529.3654030","relation":{},"subject":[],"published":{"date-parts":[[2024,7,14]]},"assertion":[{"value":"2024-07-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}