{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:20:18Z","timestamp":1759882818417,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2022ZD0116600"],"award-info":[{"award-number":["2022ZD0116600"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities","award":["14380020"],"award-info":[{"award-number":["14380020"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,14]]},"DOI":"10.1145\/3712255.3734311","type":"proceedings-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T15:14:02Z","timestamp":1754925242000},"page":"1957-1965","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["How to Train Algorithm Selection Models: Insights from Black-box Continuous Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9904-6550","authenticated-orcid":false,"given":"Xiao","family":"He","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8984-7771","authenticated-orcid":false,"given":"Haopu","family":"Shang","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6011-2512","authenticated-orcid":false,"given":"Chao","family":"Qian","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.22830"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00713"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2007.10.012"},{"key":"e_1_3_2_1_4_1","volume-title":"Advances in Neural Information Processing Systems 36 (NeurIPS'23)","author":"Shi Yunqi","year":"2023","unstructured":"Yunqi Shi, Ke Xue, Song Lei, and Chao Qian. Macro placement by wire-mask-guided black-box optimization. In Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA, 2023."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the 61th ACM\/IEEE Design Automation Conference (DAC'24)","author":"Xue Ke","year":"2024","unstructured":"Ke Xue, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, and Chao Qian. Escaping local optima in global placement. In Proceedings of the 61th ACM\/IEEE Design Automation Conference (DAC'24), San Francisco, CA, 2024."},{"key":"e_1_3_2_1_6_1","volume-title":"Springer","author":"Hansen Nikolaus","year":"2006","unstructured":"Nikolaus Hansen. The CMA evolution strategy: A comparing review. In Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms, pages 75\u2013102. Springer, 2006."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2059031"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-016-2474-6"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-5956-9"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00242"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.05.010"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3108185"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15844-5_8"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2001576.2001690"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00236"},{"key":"e_1_3_2_1_16_1","volume-title":"Learning and Intelligent Optimization - 18th International Conference (LION'18)","author":"Seiler Moritz","year":"2024","unstructured":"Moritz Seiler, Urban Skvorc, Carola Doerr, and Heike Trautmann. Synergies of deep and classical exploratory landscape features for automated algorithm selection. In Learning and Intelligent Optimization - 18th International Conference (LION'18), Ischia Island, Italy, 2024."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3512290.3528834"},{"key":"e_1_3_2_1_18_1","volume-title":"On the utility of probing trajectories for algorithm-selection. arXiv:2401.12745","author":"Renau Quentin","year":"2024","unstructured":"Quentin Renau and Emma Hart. On the utility of probing trajectories for algorithm-selection. arXiv:2401.12745, 2024."},{"key":"e_1_3_2_1_19_1","volume-title":"Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single- and multi-objective continuous optimization problems. arXiv:2401.01192","author":"Seiler Moritz Vinzent","year":"2024","unstructured":"Moritz Vinzent Seiler, Pascal Kerschke, and Heike Trautmann. Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single- and multi-objective continuous optimization problems. arXiv:2401.01192, 2024."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-14714-2_1"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2330163.2330209"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583133.3590697"},{"key":"e_1_3_2_1_25_1","volume-title":"Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical report","author":"Finck Steffen","year":"2010","unstructured":"Steffen Finck, Nikolaus Hansen, Raymond Ros, and Anne Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical report, 2010."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3638529.3654100"},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Automated Machine Learning, 12\u201315","author":"Vermetten Diederick","year":"2023","unstructured":"Diederick Vermetten, Furong Ye, Thomas B\u00e4ck, and Carola Doerr. MA-BBOB: many-affine combinations of BBOB functions for evaluating automl approaches in noiseless numerical black-box optimization contexts. In International Conference on Automated Machine Learning, 12\u201315 November 2023, Hasso Plattner Institute, Potsdam, Germany, Potsdam, Germany, 2023."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 42nd International Conference on Machine Learning (ICML'25)","author":"Gao Chengrui","year":"2025","unstructured":"Chengrui Gao, Haopu Shang, Ke Xue, and Chao Qian. Neural solver selection for combinatorial optimization. In Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Pascal Kerschke and Heike Trautmann. Comprehensive feature-based landscape analysis of continuous and constrained optimization problems using the r-package flacco. Applications in Statistical Computing: From Music Data Analysis to Industrial Quality Improvement pages 93\u2013123 2019.","DOI":"10.1007\/978-3-030-25147-5_7"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI50451.2021.9660174"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0065-2458(08)60520-3"},{"key":"e_1_3_2_1_32_1","first-page":"794","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16)","author":"Chen Tianqi","year":"2016","unstructured":"Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi, editors, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16), pages 785\u2013794, San Francisco, CA, 2016."},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the 11th International Conference on Learning Representations (ICLR'23)","author":"Hollmann Noah","year":"2023","unstructured":"Noah Hollmann, Samuel M\u00fcller, Katharina Eggensperger, and Frank Hutter. Tabpfn: A transformer that solves small tabular classification problems in a second. In Proceedings of the 11th International Conference on Learning Representations (ICLR'23), Kigali, Rwanda, 2023."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61527-7_21"},{"key":"e_1_3_2_1_35_1","volume-title":"Deep learning for multi-label learning: A comprehensive survey. arXiv:2401.16549","author":"Tarekegn Adane Nega","year":"2024","unstructured":"Adane Nega Tarekegn, Mohib Ullah, and Faouzi Alaya Cheikh. Deep learning for multi-label learning: A comprehensive survey. arXiv:2401.16549, 2024."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20862-1_40"},{"key":"e_1_3_2_1_37_1","volume-title":"Leveraging TSP solver complementarity through machine learning. Evolutionary Computation, 26(4)","author":"Kerschke Pascal","year":"2018","unstructured":"Pascal Kerschke, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike Trautmann. Leveraging TSP solver complementarity through machine learning. Evolutionary Computation, 26(4), 2018."},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 13th International Conference on Learning Representations (ICLR'25)","author":"Tan Rong-Xi","year":"2025","unstructured":"Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, Yao Wang, Yaoyuan Wang, Sheng Fu, and Chao Qian. Offline model-based optimization by learning to rank. In Proceedings of the 13th International Conference on Learning Representations (ICLR'25), Singapore, 2025."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2508063"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9466"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5694-9"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346567"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390306"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/0041-5553(67)90144-9"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835928"},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25)","author":"Song Lei","year":"2025","unstructured":"Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao, Zongzhang Zhang, and Chao Qian. Reinforced in-context black-box optimization. In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), Montreal, Canada, 2025."}],"event":{"name":"GECCO '25 Companion: Genetic and Evolutionary Computation Conference Companion","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"NH Malaga Hotel Malaga Spain","acronym":"GECCO '25 Companion"},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3712255.3734311","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T11:44:28Z","timestamp":1759837468000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3712255.3734311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,14]]},"references-count":46,"alternative-id":["10.1145\/3712255.3734311","10.1145\/3712255"],"URL":"https:\/\/doi.org\/10.1145\/3712255.3734311","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"}}]}}