{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:26:40Z","timestamp":1775143600377,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276100"],"award-info":[{"award-number":["62276100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Natural Science Foundation for Outstanding Youth Team Project","award":["2024B1515040010"],"award-info":[{"award-number":["2024B1515040010"]}]},{"name":"Guangdong Natural Science Funds for Distinguished Young Scholars","award":["2022B1515020049"],"award-info":[{"award-number":["2022B1515020049"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,14]]},"DOI":"10.1145\/3712256.3726316","type":"proceedings-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T12:26:58Z","timestamp":1751977618000},"page":"1137-1145","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6216-9379","authenticated-orcid":false,"given":"Zeyuan","family":"Ma","sequence":"first","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6499-0237","authenticated-orcid":false,"given":"Zhiyang","family":"Huang","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7539-6156","authenticated-orcid":false,"given":"Jiacheng","family":"Chen","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4499-759X","authenticated-orcid":false,"given":"Zhiguang","family":"Cao","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5648-1160","authenticated-orcid":false,"given":"Yue-Jiao","family":"Gong","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Two decades of blackbox optimization applications. EURO Journal on Computational Optimization","author":"Alarie St\u00e9phane","year":"2021","unstructured":"St\u00e9phane Alarie, Charles Audet, A\u00efmen E Gheribi, Michael Kokkolaras, and S\u00e9bastien Le Digabel. 2021. Two decades of blackbox optimization applications. EURO Journal on Computational Optimization (2021)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00163-020-00336-7"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0263150"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-017-9240-5"},{"key":"e_1_3_2_1_5_1","volume-title":"The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=vLJcd43U7a","author":"Chen Jiacheng","year":"2024","unstructured":"Jiacheng Chen, Zeyuan Ma, Hongshu Guo, Yining Ma, Jie Zhang, and Yue-Jiao Gong. 2024. SYMBOL: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=vLJcd43U7a"},{"key":"e_1_3_2_1_6_1","unstructured":"Ziwen Chen Gundavarapu and WU DI. 2024. Vision-KAN: Exploring the Possibility of KAN Replacing MLP in Vision Transformer. https:\/\/github.com\/chenziwenhaoshuai\/Vision-KAN.git."},{"key":"e_1_3_2_1_7_1","volume-title":"Differential evolution: A survey of the state-of-the-art","author":"Das Swagatam","year":"2010","unstructured":"Swagatam Das and Ponnuthurai Nagaratnam Suganthan. 2010. Differential evolution: A survey of the state-of-the-art. IEEE transactions on evolutionary computation 15, 1 (2010), 4\u201331."},{"key":"e_1_3_2_1_8_1","unstructured":"Jan Drgona Aaron Tuor James Koch Madelyn Shapiro Bruno Jacob and Draguna Vrabie. 2023. NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations. (2023). https:\/\/github.com\/pnnl\/neuromancer"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2023.3338693"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution","author":"Guo Hongshu","year":"2024","unstructured":"Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, and Yue-Jiao Gong. 2024. Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2024)."},{"key":"e_1_3_2_1_11_1","volume-title":"ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning. arXiv preprint arXiv:2412.07507","author":"Guo Hongshu","year":"2024","unstructured":"Hongshu Guo, Zeyuan Ma, Jiacheng Chen, Yining Ma, Zhiguang Cao, Xinglin Zhang, and Yue-Jiao Gong. 2024. ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning. arXiv preprint arXiv:2412.07507 (2024)."},{"key":"e_1_3_2_1_12_1","volume-title":"The CMA evolution strategy: A tutorial. arXiv preprint arXiv:1604.00772","author":"Hansen Nikolaus","year":"2016","unstructured":"Nikolaus Hansen. 2016. The CMA evolution strategy: A tutorial. arXiv preprint arXiv:1604.00772 (2016)."},{"key":"e_1_3_2_1_13_1","unstructured":"Nikolaus Hansen Anne Auger Steffen Finck and Raymond Ros. 2010. Realparameter black-box optimization benchmarking 2010: Experimental setup. Ph. D. Dissertation. INRIA."},{"key":"e_1_3_2_1_14_1","volume-title":"A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms. arXiv preprint arXiv:2405.16494","author":"Hao Hao","year":"2024","unstructured":"Hao Hao, Xiaoqun Zhang, Bingdong Li, and Aimin Zhou. 2024. A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms. arXiv preprint arXiv:2405.16494 (2024)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3170638"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2018.2869001"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622737.1622748"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11590-019-01428-7"},{"key":"e_1_3_2_1_20_1","volume-title":"David Echeverr\u00eda Ciaurri, and Leifur Leifsson","author":"Koziel Slawomir","year":"2011","unstructured":"Slawomir Koziel, David Echeverr\u00eda Ciaurri, and Leifur Leifsson. 2011. Surrogate-based methods. Computational optimization, methods and algorithms (2011), 33\u201359."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583131.3590496"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583133.3595822"},{"key":"e_1_3_2_1_23_1","volume-title":"Pretrained Optimization Model for Zero-Shot Black Box Optimization. In The Thirty-eighth Annual Conference on Neural Information Processing Systems.","author":"Li Xiaobin","year":"2024","unstructured":"Xiaobin Li, Kai Wu, Yujian Betterest Li, Xiaoyu Zhang, Handing Wang, and Jing Liu. 2024. Pretrained Optimization Model for Zero-Shot Black Box Optimization. In The Thirty-eighth Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_24_1","volume-title":"B2Opt: Learning to Optimize Black-box Optimization with Little Budget. arXiv preprint arXiv:2304.11787","author":"Li Xiaobin","year":"2023","unstructured":"Xiaobin Li, Kai Wu, Xiaoyu Zhang, Handing Wang, and Jing Liu. 2023. B2Opt: Learning to Optimize Black-box Optimization with Little Budget. arXiv preprint arXiv:2304.11787 (2023)."},{"key":"e_1_3_2_1_25_1","volume-title":"KAN: Kolmogorov-Arnold Networks. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Ozo7qJ5vZi","author":"Liu Ziming","year":"2025","unstructured":"Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, and Max Tegmark. 2025. KAN: Kolmogorov-Arnold Networks. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Ozo7qJ5vZi"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3638529.3653996"},{"key":"e_1_3_2_1_27_1","volume-title":"MetaBox: a benchmark platform for meta-black-box optimization with reinforcement learning. Advances in Neural Information Processing Systems 36","author":"Ma Zeyuan","year":"2024","unstructured":"Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng, Yue-Jiao Gong, Yining Ma, and Zhiguang Cao. 2024. MetaBox: a benchmark platform for meta-black-box optimization with reinforcement learning. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_28_1","volume-title":"Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization. arXiv preprint arXiv:2411.00625","author":"Ma Zeyuan","year":"2024","unstructured":"Zeyuan Ma, Hongshu Guo, Yue-Jiao Gong, Jun Zhang, and Kay Chen Tan. 2024. Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization. arXiv preprint arXiv:2411.00625 (2024)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2001576.2001690"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07788-z"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.birob.2024.100184"},{"key":"e_1_3_2_1_32_1","volume-title":"Smooth Kolmogorov Arnold networks enabling structural knowledge representation. arXiv preprint arXiv:2405.11318","author":"Samadi Moein E","year":"2024","unstructured":"Moein E Samadi, Younes M\u00fcller, and Andreas Schuppert. 2024. Smooth Kolmogorov Arnold networks enabling structural knowledge representation. arXiv preprint arXiv:2405.11318 (2024)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3321707.3321813"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3060811"},{"key":"e_1_3_2_1_35_1","volume-title":"Reinforcement learning: An introduction. A Bradford Book","author":"Sutton Richard S","year":"2018","unstructured":"Richard S Sutton. 2018. Reinforcement learning: An introduction. A Bradford Book (2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=sb1HgVDLjN","author":"Tan Rong-Xi","year":"2025","unstructured":"Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, yaowang, Yaoyuan Wang, Fu Sheng, and Chao Qian. 2025. Offline Model-Based Optimization by Learning to Rank. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=sb1HgVDLjN"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107678"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.07.008"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.2514\/6.1998-4800"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.08.036"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"e_1_3_2_1_42_1","volume-title":"Gaussian processes for regression. Advances in neural information processing systems 8","author":"Williams Christopher","year":"1995","unstructured":"Christopher Williams and Carl Rasmussen. 1995. Gaussian processes for regression. Advances in neural information processing systems 8 (1995)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_13"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101838"},{"key":"e_1_3_2_1_45_1","volume-title":"Kan or mlp: A fairer comparison. arXiv preprint arXiv:2407.16674","author":"Yu Runpeng","year":"2024","unstructured":"Runpeng Yu, Weihao Yu, and Xinchao Wang. 2024. Kan or mlp: A fairer comparison. arXiv preprint arXiv:2407.16674 (2024)."}],"event":{"name":"GECCO '25: Genetic and Evolutionary Computation Conference","location":"NH Malaga Hotel Malaga Spain","acronym":"GECCO '25","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\/pdf\/10.1145\/3712256.3726316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T20:37:00Z","timestamp":1759869420000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3712256.3726316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,13]]},"references-count":45,"alternative-id":["10.1145\/3712256.3726316","10.1145\/3712256"],"URL":"https:\/\/doi.org\/10.1145\/3712256.3726316","relation":{},"subject":[],"published":{"date-parts":[[2025,7,13]]},"assertion":[{"value":"2025-07-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}