{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T12:40:10Z","timestamp":1770468010076,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":38,"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\/3638529.3654052","type":"proceedings-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T16:33:04Z","timestamp":1720456384000},"page":"394-402","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-objective evolutionary GAN for tabular data synthesis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9418-3412","authenticated-orcid":false,"given":"Nian","family":"Ran","sequence":"first","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6526-7185","authenticated-orcid":false,"given":"Bahrul","family":"Nasution","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4803-3007","authenticated-orcid":false,"given":"Claire","family":"Little","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1236-3143","authenticated-orcid":false,"given":"Richard","family":"Allmendinger","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3142-4493","authenticated-orcid":false,"given":"Mark","family":"Elliot","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,7,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"211","author":"Albuquerque Isabela","year":"2019","unstructured":"Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, and Ioannis Mitliagkas. 2019. Multi-objective training of Generative Adversarial Networks with multiple discriminators. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 202--211. https:\/\/proceedings.mlr.press\/v97\/albuquerque19a.html"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","unstructured":"Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein GAN. 10.48550\/ARXIV.1701.07875","DOI":"10.48550\/ARXIV.1701.07875"},{"key":"e_1_3_2_1_3_1","volume-title":"International conference on machine learning. PMLR, 224--232","author":"Arora Sanjeev","year":"2017","unstructured":"Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. 2017. Generalization and equilibrium in generative adversarial nets (gans). In International conference on machine learning. PMLR, 224--232."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC45853.2021.9504858"},{"key":"e_1_3_2_1_5_1","volume-title":"Integrated Public Use Microdata Series","author":"Minnesota Population Center","year":"2020","unstructured":"Minnesota Population Center. 2020. Integrated Public Use Microdata Series, International: Version 7.3 [dataset]. Minneapolis, MN: IPUMS. IPUMs Census Data (2020)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2010.ap09480"},{"key":"e_1_3_2_1_8_1","first-page":"1","article-title":"Evolutionary Variational Optimization of Generative Models","volume":"23","author":"Drefs Jakob","year":"2022","unstructured":"Jakob Drefs, Enrico Guiraud, and J\u00f6rg L\u00fccke. 2022. Evolutionary Variational Optimization of Generative Models. Journal of Machine Learning Research 23, 21 (2022), 1--51. http:\/\/jmlr.org\/papers\/v23\/20-233.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_9_1","volume-title":"A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural computing 17","author":"Emmerich Michael TM","year":"2018","unstructured":"Michael TM Emmerich and Andr\u00e9 H Deutz. 2018. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural computing 17 (2018), 585--609."},{"key":"e_1_3_2_1_10_1","volume-title":"Weinberger (Eds.)","volume":"27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger (Eds.), Vol. 27. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2022.3155308"},{"key":"e_1_3_2_1_12_1","volume-title":"Garnett (Eds.)","volume":"30","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017. Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf"},{"key":"e_1_3_2_1_13_1","volume-title":"Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkE3y85ee","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkE3y85ee"},{"key":"e_1_3_2_1_14_1","volume-title":"The Synthetic Data Challenge. Conference of European Statisticians (2019","author":"Jennifer Taub","year":"2019","unstructured":"Taub Jennifer and Elliot Mark. 2019. The Synthetic Data Challenge. Conference of European Statisticians (2019). https:\/\/unece.org\/fileadmin\/DAM\/stats\/documents\/ece\/ces\/ge.46\/2019\/mtg1\/SDC2019_S3_UK_Synthethic_Data_Challenge_Elliot_AD.pdf"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1198\/000313006X124640"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2983071"},{"key":"e_1_3_2_1_17_1","volume-title":"Privacy in Statistical Databases, Josep Domingo-Ferrer and Maryline Laurent (Eds.)","author":"Little Claire","unstructured":"Claire Little, Mark Elliot, and Richard Allmendinger. 2022. Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata. In Privacy in Statistical Databases, Josep Domingo-Ferrer and Maryline Laurent (Eds.). Springer International Publishing, Cham, 234--249."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.23889\/ijpds.v8i1.2158"},{"key":"e_1_3_2_1_19_1","volume-title":"Synthetic census microdata generation: A comparative study of synthesis methods examining the trade-off between disclosure risk and utility. Journal of Official Statistics","author":"Little Claire","year":"2024","unstructured":"Claire Little, Mark Elliot, and Richard Allmendinger. 2024. Synthetic census microdata generation: A comparative study of synthesis methods examining the trade-off between disclosure risk and utility. Journal of Official Statistics (2024)."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Ma Chao","year":"2020","unstructured":"Chao Ma, Sebastian Tschiatschek, Richard Turner, Jos\u00e9 Miguel Hern\u00e1ndez-Lobato, and Cheng Zhang. 2020. VAEM: A Deep Generative Model for Heterogeneous Mixed Type Data. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS'20). Curran Associates Inc., Red Hook, NY, USA, Article 943, 11 pages."},{"key":"e_1_3_2_1_21_1","volume-title":"Adversarial Autoencoders. In International Conference on Learning Representations. http:\/\/arxiv.org\/abs\/1511","author":"Makhzani Alireza","year":"2016","unstructured":"Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow. 2016. Adversarial Autoencoders. In International Conference on Learning Representations. http:\/\/arxiv.org\/abs\/1511.05644"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISC255366.2022.9921948"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110738"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v074.i11"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. F-GAN: Training Generative Neural Samplers Using Variational Divergence Minimization. In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS'16). Curran Associates Inc., Red Hook, NY, USA, 271--279."},{"key":"e_1_3_2_1_27_1","volume-title":"Census Division and University of Manchester","author":"Office for National Statistics","year":"2013","unstructured":"Office for National Statistics, Census Division and University of Manchester, Cathie Marsh Centre for Census and Survey Research. 2013. Census 1991: Individual Sample of Anonymised Records for Great Britain (SARs). (2013)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3091117"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1068\/a38335"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","unstructured":"Alec Radford Luke Metz and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. 10.48550\/ARXIV.1511.06434","DOI":"10.48550\/ARXIV.1511.06434"},{"key":"e_1_3_2_1_31_1","unstructured":"Yunus Saatci and Andrew G Wilson. 2017. Bayesian GAN I Guyon U Von Luxburg S Bengio H Wallach R Fergus S Vishwanathan and R Garnett (Eds.). Advances in Neural Information Processing Systems 30. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/312351bff07989769097660a56395065-Paper.pdf"},{"key":"e_1_3_2_1_32_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-93158-2"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1811.11357"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2895748"},{"key":"e_1_3_2_1_36_1","unstructured":"Zhendong Wang Huangjie Zheng Pengcheng He Weizhu Chen and Mingyuan Zhou. 2023. Diffusion-GAN: Training GANs with Diffusion. arXiv:2206.02262 [cs.LG]"},{"key":"e_1_3_2_1_37_1","volume-title":"Garnett (Eds.)","volume":"32","author":"Xu Lei","year":"2019","unstructured":"Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling Tabular data using Conditional GAN. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/254ed7d2de3b23ab10936522dd547b78-Paper.pdf"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-36622-2_32"}],"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\/3638529.3654052","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638529.3654052","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:56:50Z","timestamp":1750291010000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638529.3654052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,14]]},"references-count":38,"alternative-id":["10.1145\/3638529.3654052","10.1145\/3638529"],"URL":"https:\/\/doi.org\/10.1145\/3638529.3654052","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"}}]}}