{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:53:12Z","timestamp":1742975592618,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030788100"},{"type":"electronic","value":"9783030788117"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78811-7_33","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:22:37Z","timestamp":1625613757000},"page":"339-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Parallel Random Embedding with Negatively Correlated Search"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0127-6167","authenticated-orcid":false,"given":"Qi","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5333-6155","authenticated-orcid":false,"given":"Peng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6236-2002","authenticated-orcid":false,"given":"Ke","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"unstructured":"Al-Dujaili, A., Suresh, S.: Embedded bandits for large-scale black-box optimization. In: Singh, S.P., Markovitch, S. (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4\u20139, 2017, San Francisco, California, USA. pp. 758\u2013764. AAAI Press, New York (2017)","key":"33_CR1"},{"issue":"1","key":"33_CR2","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10898-019-00839-1","volume":"76","author":"M Binois","year":"2019","unstructured":"Binois, M., Ginsbourger, D., Roustant, O.: On the choice of the low-dimensional domain for global optimization via random embeddings. J. Global Optim. 76(1), 69\u201390 (2019). https:\/\/doi.org\/10.1007\/s10898-019-00839-1","journal-title":"J. Global Optim."},{"key":"33_CR3","first-page":"190","volume":"22","author":"A Carpentier","year":"2012","unstructured":"Carpentier, A., Munos, R.: Bandit theory meets compressed sensing for high dimensional stochastic linear bandit. Proc. Mach. Learn. Res. 22, 190\u2013198 (2012)","journal-title":"Proc. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Chrabaszcz, P., Loshchilov, I., Hutter, F.: Back to basics: Benchmarking canonical evolution strategies for playing atari. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 1419\u20131426 (2018)","key":"33_CR4","DOI":"10.24963\/ijcai.2018\/197"},{"unstructured":"Conti, E., Madhavan, V., Such, F.P., Lehman, J., Stanley, K.O., Clune, J.: Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In: Advances in Neural Information Processing Systems 31: NeurIPS 2018, December 3\u20138, 2018, Montreal, Canada. pp. 5032\u20135043 (2018)","key":"33_CR5"},{"doi-asserted-by":"crossref","unstructured":"Kaban, A., Bootkrajang, J., Durrant, R.J.: Towards large scale continuous EDA: a random matrix theory perspective. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO 2013, p. 383. ACM Press, New York (2013)","key":"33_CR6","DOI":"10.1145\/2463372.2463423"},{"unstructured":"Kakade, S.M.: A natural policy gradient. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3\u20138, 2001, Vancouver, British Columbia, Canada], pp. 1531\u20131538. MIT Press, Cambridge, MA (2001)","key":"33_CR7"},{"unstructured":"Knight, J.N., Lunacek, M.: Reducing the space-time complexity of the CMA-ES. In: Lipson, H. (ed.) Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings, London, England, UK, July 7\u201311, 2007, pp. 658\u2013665. ACM Press, New York (2007)","key":"33_CR8"},{"doi-asserted-by":"crossref","unstructured":"Loshchilov, I.: A computationally efficient limited memory CMA-ES for large scale optimization. In: Arnold, D.V. (ed.) Genetic and Evolutionary Computation Conference, pp. 397\u2013404. ACM Press, New York (2014)","key":"33_CR9","DOI":"10.1145\/2576768.2598294"},{"issue":"3","key":"33_CR10","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TEVC.2018.2868770","volume":"23","author":"X Ma","year":"2019","unstructured":"Ma, X., et al.: A survey on cooperative co-evolutionary algorithms. IEEE Trans. Evol. Comput. 23(3), 421\u2013441 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"33_CR11","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1613\/jair.5699","volume":"61","author":"MC Machado","year":"2018","unstructured":"Machado, M.C., Bellemare, M.G., et al.: Revisiting the arcade learning environment: evaluation protocols and open problems for general agents. J. Artif. Intell. Res. 61(1), 523\u2013562 (2018)","journal-title":"J. Artif. Intell. Res."},{"key":"33_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/978-3-319-99259-4_33","volume-title":"Parallel Problem Solving from Nature","author":"N M\u00fcller","year":"2018","unstructured":"M\u00fcller, N., Glasmachers, T.: Challenges in high-dimensional reinforcement learning with evolution strategies. In: Auger, A., Fonseca, C.M., Louren\u00e7o, N., Machado, P., Paquete, L., Whitley, D. (eds.) PPSN 2018. LNCS, vol. 11102, pp. 411\u2013423. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99259-4_33"},{"issue":"7540","key":"33_CR13","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"33_CR14","first-page":"1928","volume":"48","author":"V Mnih","year":"2016","unstructured":"Mnih, V., Badia, A.P., et al.: Asynchronous methods for deep reinforcement learning. Proc. Mach. Learn. Res. 48, 1928\u20131937 (2016)","journal-title":"Proc. Mach. Learn. Res."},{"key":"33_CR15","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1007\/978-3-319-56991-8_32","volume-title":"Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016","author":"SS Mousavi","year":"2018","unstructured":"Mousavi, S.S., Schukat, M., Howley, E.: Deep reinforcement learning: an overview. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 16, pp. 426\u2013440. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-56991-8_32"},{"unstructured":"Qian, H., Hu, Y.Q., Yu, Y.: Derivative-free optimization of high-dimensional non-convex functions by sequential random embeddings. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 1946\u20131952. AAAI Press, New York (2016)","key":"33_CR16"},{"doi-asserted-by":"crossref","unstructured":"Qian, H., Yu, Y.: Scaling simultaneous optimistic optimization for high-dimensional non-convex functions with low effective dimensions. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2000\u20132006. AAAI Press, New York (2016)","key":"33_CR17","DOI":"10.1609\/aaai.v30i1.10288"},{"doi-asserted-by":"crossref","unstructured":"Qian, H., Yu, Y.: Solving high-dimensional multi-objective optimization problems with low effective dimensions. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI 2017, pp. 875\u2013881. AAAI Press, New York (2017)","key":"33_CR18","DOI":"10.1609\/aaai.v31i1.10664"},{"unstructured":"Salimans, T., Ho, J., et al.: Evolution strategies as a scalable alternative to reinforcement learning. arXiv:1703.03864 (2017)","key":"33_CR19"},{"key":"33_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/978-3-319-45823-6_80","volume-title":"Parallel Problem Solving from Nature","author":"ML Sanyang","year":"2016","unstructured":"Sanyang, M.L., Kab\u00e1n, A.: REMEDA: random embedding EDA for optimising functions with intrinsic dimension. In: Handl, J., Hart, E., Lewis, P.R., L\u00f3pez-Ib\u00e1\u00f1ez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 859\u2013868. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-45823-6_80"},{"unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv:1707.06347 (2017)","key":"33_CR21"},{"unstructured":"Such, F.P., Madhavan, V., et al.: Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv:1712.06567 (2018)","key":"33_CR22"},{"issue":"3","key":"33_CR23","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1109\/JSAC.2016.2525458","volume":"34","author":"K Tang","year":"2016","unstructured":"Tang, K., Yang, P., Yao, X.: Negatively correlated search. IEEE J. Sel. Areas Commun. 34(3), 542\u2013550 (2016)","journal-title":"IEEE J. Sel. Areas Commun."},{"unstructured":"Wang, Z., Zoghi, M., et al.: Bayesian optimization in high dimensions via random embeddings. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 1778\u20131784. AAAI Press (2013)","key":"33_CR24"},{"key":"33_CR25","doi-asserted-by":"publisher","first-page":"163105","DOI":"10.1109\/ACCESS.2019.2938765","volume":"7","author":"P Yang","year":"2019","unstructured":"Yang, P., Tang, K., Yao, X.: A parallel divide-and-conquer-based evolutionary algorithm for large-scale optimization. IEEE Access 7, 163105\u2013163118 (2019)","journal-title":"IEEE Access"},{"issue":"15","key":"33_CR26","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.1016\/j.ins.2008.02.017","volume":"178","author":"Z Yang","year":"2008","unstructured":"Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985\u20132999 (2008)","journal-title":"Inf. Sci."},{"unstructured":"Zhang, L., Mahdavi, M., Jin, R., Yang, T., Zhu, S.: Recovering the optimal solution by dual random projection. In: Proceedings of the 26th Annual Conference on Learning Theory, vol. 30, pp. 135\u2013157 (2013)","key":"33_CR27"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78811-7_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T02:26:52Z","timestamp":1672712812000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78811-7_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030788100","9783030788117"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78811-7_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"104","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4-5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}