{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:48:26Z","timestamp":1772779706816,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":3,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T00:00:00Z","timestamp":1689379200000},"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":[[2023,7,15]]},"DOI":"10.1145\/3583133.3595822","type":"proceedings-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T23:30:33Z","timestamp":1690241433000},"page":"29-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Discovering Evolution Strategies via Meta-Black-Box Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8799-1138","authenticated-orcid":false,"given":"Robert","family":"Lange","sequence":"first","affiliation":[{"name":"Technical Univ. Berlin, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2961-8782","authenticated-orcid":false,"given":"Tom","family":"Schaul","sequence":"additional","affiliation":[{"name":"Google DeepMind, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5202-0054","authenticated-orcid":false,"given":"Yutian","family":"Chen","sequence":"additional","affiliation":[{"name":"Google DeepMind, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2309-922X","authenticated-orcid":false,"given":"Tom","family":"Zahavy","sequence":"additional","affiliation":[{"name":"Google DeepMind, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4944-6745","authenticated-orcid":false,"given":"Valentin","family":"Dalibard","sequence":"additional","affiliation":[{"name":"Google DeepMind, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4730-3633","authenticated-orcid":false,"given":"Chris","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5169-9486","authenticated-orcid":false,"given":"Satinder","family":"Singh","sequence":"additional","affiliation":[{"name":"Google DeepMind, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2354-4193","authenticated-orcid":false,"given":"Sebastian","family":"Flennerhag","sequence":"additional","affiliation":[{"name":"Google DeepMind, London, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR","author":"Lange Robert Tjarko","year":"2023","unstructured":"Robert Tjarko Lange , Tom Schaul , Yutian Chen , Tom Zahavy , Valentin Dallibard , Chris Lu , Satinder Singh , and Sebastian Flennerhag . 2023. Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR ( 2023 ). Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu, Satinder Singh, and Sebastian Flennerhag. 2023. Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR (2023)."},{"key":"e_1_3_2_1_2_1","volume-title":"International conference on machine learning. PMLR.","author":"Lee Juho","year":"2019","unstructured":"Juho Lee , Yoonho Lee , Jungtaek Kim , Adam Kosiorek , Seungjin Choi , and Yee Whye Teh . 2019 . Set transformer: A framework for attention-based permutation-invariant neural networks . In International conference on machine learning. PMLR. Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. 2019. Set transformer: A framework for attention-based permutation-invariant neural networks. In International conference on machine learning. PMLR."},{"key":"e_1_3_2_1_3_1","volume-title":"Practical tradeoffs between memory, compute, and performance in learned optimizers. arXiv preprint arXiv:2203.11860","author":"Metz Luke","year":"2022","unstructured":"Luke Metz , C Daniel Freeman , James Harrison , Niru Maheswaranathan , and Jascha Sohl-Dickstein . 2022. Practical tradeoffs between memory, compute, and performance in learned optimizers. arXiv preprint arXiv:2203.11860 ( 2022 ). Luke Metz, C Daniel Freeman, James Harrison, Niru Maheswaranathan, and Jascha Sohl-Dickstein. 2022. Practical tradeoffs between memory, compute, and performance in learned optimizers. arXiv preprint arXiv:2203.11860 (2022)."}],"event":{"name":"GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation","location":"Lisbon Portugal","acronym":"GECCO '23 Companion","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"]},"container-title":["Proceedings of the Companion Conference on Genetic and Evolutionary Computation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583133.3595822","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583133.3595822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:50Z","timestamp":1750178270000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583133.3595822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,15]]},"references-count":3,"alternative-id":["10.1145\/3583133.3595822","10.1145\/3583133"],"URL":"https:\/\/doi.org\/10.1145\/3583133.3595822","relation":{},"subject":[],"published":{"date-parts":[[2023,7,15]]},"assertion":[{"value":"2023-07-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}