{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:03:17Z","timestamp":1770778997658,"version":"3.50.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031700675","type":"print"},{"value":"9783031700682","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70068-2_23","type":"book-chapter","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T23:02:54Z","timestamp":1725663774000},"page":"374-390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evolve Cost-Aware Acquisition Functions Using Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7069-6304","authenticated-orcid":false,"given":"Yiming","family":"Yao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6719-0409","authenticated-orcid":false,"given":"Fei","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1123-6030","authenticated-orcid":false,"given":"Ji","family":"Cheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0786-0671","authenticated-orcid":false,"given":"Qingfu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,7]]},"reference":[{"key":"23_CR1","unstructured":"Bai, T., Li, Y., Shen, Y., Zhang, X., Zhang, W., Cui, B.: Transfer learning for bayesian optimization: a survey. arXiv preprint arXiv:2302.05927 (2023)"},{"key":"23_CR2","first-page":"21524","volume":"33","author":"M Balandat","year":"2020","unstructured":"Balandat, M., et al.: Botorch: a framework for efficient monte-carlo bayesian optimization. Adv. Neural. Inf. Process. Syst. 33, 21524\u201321538 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"23_CR3","first-page":"38788","volume":"35","author":"A Bansal","year":"2022","unstructured":"Bansal, A., Stoll, D., Janowski, M., Zela, A., Hutter, F.: JAHS-bench-201: a foundation for research on joint architecture and hyperparameter search. Adv. Neural. Inf. Process. Syst. 35, 38788\u201338802 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"23_CR4","unstructured":"Chen, Y., et al.: Learning to learn without gradient descent by gradient descent. In: International Conference on Machine Learning, pp. 748\u2013756. PMLR (2017)"},{"key":"23_CR5","first-page":"32053","volume":"35","author":"Y Chen","year":"2022","unstructured":"Chen, Y., et al.: Towards learning universal hyperparameter optimizers with transformers. Adv. Neural. Inf. Process. Syst. 35, 32053\u201332068 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"23_CR6","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1137\/070693424","volume":"47","author":"PI Frazier","year":"2008","unstructured":"Frazier, P.I., Powell, W.B., Dayanik, S.: A knowledge-gradient policy for sequential information collection. SIAM J. Control. Optim. 47(5), 2410\u20132439 (2008)","journal-title":"SIAM J. Control. Optim."},{"key":"23_CR7","series-title":"Springer Series in Materials Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-23871-5_3","volume-title":"Information Science for Materials Discovery and Design","author":"PI Frazier","year":"2016","unstructured":"Frazier, P.I., Wang, J.: Bayesian optimization for materials design. In: Lookman, T., Alexander, F.J., Rajan, K. (eds.) Information Science for Materials Discovery and Design. SSMS, vol. 225, pp. 45\u201375. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-23871-5_3"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Garnett, R., Osborne, M.A., Roberts, S.J.: Bayesian optimization for sensor set selection. In: Proceedings of the 9th ACM\/IEEE International Conference on Information Processing in Sensor Networks, pp. 209\u2013219 (2010)","DOI":"10.1145\/1791212.1791238"},{"key":"23_CR9","unstructured":"Guinet, G., Perrone, V., Archambeau, C.: Pareto-efficient acquisition functions for cost-aware bayesian optimization. arXiv preprint arXiv:2011.11456 (2020)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947\u2013951 (2000)","DOI":"10.1038\/35016072"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Hemberg, E., Moskal, S., O\u2019Reilly, U.M.: Evolving code with a large language model. arXiv preprint arXiv:2401.07102 (2024)","DOI":"10.1007\/s10710-024-09494-2"},{"key":"23_CR12","first-page":"7718","volume":"34","author":"BJ Hsieh","year":"2021","unstructured":"Hsieh, B.J., Hsieh, P.C., Liu, X.: Reinforced few-shot acquisition function learning for bayesian optimization. Adv. Neural. Inf. Process. Syst. 34, 7718\u20137731 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"23_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep27988","volume":"6","author":"JN Kather","year":"2016","unstructured":"Kather, J.N., Weis, C.A., Bianconi, F., Melchers, S.M., Schad, L.R., Gaiser, T., Marx, A., Z\u00f6llner, F.G.: Multi-class texture analysis in colorectal cancer histology. Sci. Rep. 6(1), 1\u201311 (2016)","journal-title":"Sci. Rep."},{"key":"23_CR14","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)"},{"issue":"1","key":"23_CR15","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1115\/1.3653121","volume":"86","author":"HJ Kushner","year":"1964","unstructured":"Kushner, H.J.: A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J. Basic Eng. 86(1), 97\u2013106 (1964)","journal-title":"J. Basic Eng."},{"key":"23_CR16","unstructured":"Lee, E.H., Eriksson, D., Perrone, V., Seeger, M.: A nonmyopic approach to cost-constrained bayesian optimization. In: Uncertainty in Artificial Intelligence, pp. 568\u2013577. PMLR (2021)"},{"key":"23_CR17","unstructured":"Lee, E.H., Perrone, V., Archambeau, C., Seeger, M.: Cost-aware bayesian optimization. arXiv preprint arXiv:2003.10870 (2020)"},{"key":"23_CR18","doi-asserted-by":"publisher","unstructured":"Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., Stanley, K.O.: Evolution through large models. In: Banzhaf, W., Machado, P., Zhang, M. (eds.) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-99-3814-8_11","DOI":"10.1007\/978-981-99-3814-8_11"},{"key":"23_CR19","unstructured":"Liu, F., Tong, X., Yuan, M., Lin, X., Luo, F., Wang, Z., Lu, Z., Zhang, Q.: Evolution of heuristics: towards efficient automatic algorithm design using large language model. In: Proceedings of International Conference on Machine Learning (2024)"},{"key":"23_CR20","unstructured":"Liu, F., Tong, X., Yuan, M., Zhang, Q.: Algorithm evolution using large language model. arXiv preprint arXiv:2311.15249 (2023)"},{"key":"23_CR21","unstructured":"Liu, T., Astorga, N., Seedat, N., van\u00a0der Schaar, M.: Large language models to enhance bayesian optimization. arXiv preprint arXiv:2402.03921 (2024)"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Liventsev, V., Grishina, A., H\u00e4rm\u00e4, A., Moonen, L.: Fully autonomous programming with large language models. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1146\u20131155 (2023)","DOI":"10.1145\/3583131.3590481"},{"key":"23_CR23","doi-asserted-by":"publisher","first-page":"107481","DOI":"10.1016\/j.knosys.2021.107481","volume":"232","author":"P Luong","year":"2021","unstructured":"Luong, P., Nguyen, D., Gupta, S., Rana, S., Venkatesh, S.: Adaptive cost-aware bayesian optimization. Knowl.-Based Syst. 232, 107481 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"23_CR24","unstructured":"Maraval, A., Zimmer, M., Grosnit, A., Bou\u00a0Ammar, H.: End-to-end meta-bayesian optimisation with transformer neural processes. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"23_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/3-540-07165-2_55","volume-title":"Optimization Techniques IFIP Technical Conference Novosibirsk, July 1\u20137, 1974","author":"J Mo\u010dkus","year":"1975","unstructured":"Mo\u010dkus, J.: On bayesian methods for seeking the extremum. In: Marchuk, G.I. (ed.) Optimization Techniques 1974. LNCS, vol. 27, pp. 400\u2013404. Springer, Heidelberg (1975). https:\/\/doi.org\/10.1007\/3-540-07165-2_55"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"M\u00fcller, S.G., Hutter, F.: Trivialaugment: tuning-free yet state-of-the-art data augmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 774\u2013782 (2021)","DOI":"10.1109\/ICCV48922.2021.00081"},{"issue":"3","key":"23_CR27","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1287\/ijoc.1100.0417","volume":"23","author":"DM Negoescu","year":"2011","unstructured":"Negoescu, D.M., Frazier, P.I., Powell, W.B.: The knowledge-gradient algorithm for sequencing experiments in drug discovery. Informs J. Comput. 23(3), 346\u2013363 (2011)","journal-title":"Informs J. Comput."},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Qian, W., He, Z., Li, L., Liu, X., Gao, F.: Cobabo: a hyperparameter search method with cost budget awareness. In: 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), pp. 408\u2013412. IEEE (2021)","DOI":"10.1109\/CCIS53392.2021.9754655"},{"issue":"7995","key":"23_CR29","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1038\/s41586-023-06924-6","volume":"625","author":"B Romera-Paredes","year":"2024","unstructured":"Romera-Paredes, B., et al.: Mathematical discoveries from program search with large language models. Nature 625(7995), 468\u2013475 (2024)","journal-title":"Nature"},{"key":"23_CR30","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"23_CR31","unstructured":"Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.: Gaussian process optimization in the bandit setting: no regret and experimental design. arXiv preprint arXiv:0912.3995 (2009)"},{"key":"23_CR32","unstructured":"Turner, R., et al.: Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020. In: NeurIPS 2020 Competition and Demonstration Track, pp. 3\u201326. PMLR (2021)"},{"key":"23_CR33","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1007\/978-3-030-46147-8_22","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"V TV","year":"2020","unstructured":"TV, V., Malhotra, P., Narwariya, J., Vig, L., Shroff, G.: Meta-learning for black-box optimization. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 366\u2013381. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46147-8_22"},{"key":"23_CR34","unstructured":"Volpp, M., et al.: Meta-learning acquisition functions for transfer learning in bayesian optimization. arXiv preprint arXiv:1904.02642 (2019)"},{"key":"23_CR35","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"23_CR36","unstructured":"Zhang, M.R., Desai, N., Bae, J., Lorraine, J., Ba, J.: Using large language models for hyperparameter optimization. In: NeurIPS 2023 Foundation Models for Decision Making Workshop (2023)"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVIII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70068-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T21:40:10Z","timestamp":1732743610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70068-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031700675","9783031700682"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70068-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"7 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing interest that could appear to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hagenberg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2024.fh-ooe.at\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}