{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T03:12:38Z","timestamp":1770520358054,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030581114","type":"print"},{"value":"9783030581121","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58112-1_17","type":"book-chapter","created":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T16:04:30Z","timestamp":1599062670000},"page":"243-256","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Parallelized Bayesian Optimization for\u00a0Expensive Robot Controller Evolution"],"prefix":"10.1007","author":[{"given":"Margarita","family":"Rebolledo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederik","family":"Rehbach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. E.","family":"Eiben","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Bartz-Beielstein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., Lang, M.: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions (2017)","DOI":"10.32614\/CRAN.package.mlrMBO"},{"key":"17_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-319-09584-4_17","volume-title":"Learning and Intelligent Optimization","author":"B Bischl","year":"2014","unstructured":"Bischl, B., Wessing, S., Bauer, N., Friedrichs, K., Weihs, C.: MOI-MBO: multiobjective infill for parallel model-based optimization. In: Pardalos, P.M., Resende, M.G.C., Vogiatzis, C., Walteros, J.L. (eds.) LION 2014. LNCS, vol. 8426, pp. 173\u2013186. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-09584-4_17"},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1145\/2723872.2723882","volume":"49","author":"C Boettiger","year":"2015","unstructured":"Boettiger, C.: An introduction to docker for reproducible research. ACM SIGOPS Oper. Syst. Rev. 49(1), 71\u201379 (2015)","journal-title":"ACM SIGOPS Oper. Syst. Rev."},{"issue":"8","key":"17_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1145\/2493883","volume":"56","author":"J Bongard","year":"2013","unstructured":"Bongard, J.: Evolutionary robotics. Commun. ACM 56(8), 74\u201385 (2013)","journal-title":"Commun. ACM"},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac, J., Garc\u00eda, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3\u201318 (2011). https:\/\/doi.org\/10.1016\/j.swevo.2011.02.002","journal-title":"Swarm Evol. Comput."},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Eiben, A., et al.: The triangle of life: evolving robots in real-time and real-space, September 2013. https:\/\/doi.org\/10.7551\/978-0-262-31709-2-ch157","DOI":"10.7551\/978-0-262-31709-2-ch157"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1007\/978-3-540-30301-5_62","volume-title":"Handbook of Robotics","author":"D Floreano","year":"2008","unstructured":"Floreano, D., Husbands, P., Nolfi, S.: Evolutionary robotics. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, 1st edn, pp. 1423\u20131451. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-30301-5_62","edition":"1"},{"key":"17_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/9780470770801","volume-title":"Engineering Design via Surrogate Modelling: A Practical Guide","author":"A Forrester","year":"2008","unstructured":"Forrester, A., Sobester, A., Keane, A.: Engineering Design via Surrogate Modelling: A Practical Guide. Wiley, Hoboken (2008)"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Frazier, P.I.: A tutorial on Bayesian optimization (2018)","DOI":"10.1287\/educ.2018.0188"},{"key":"17_CR10","series-title":"Adaptation Learning and Optimization","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-642-10701-6_6","volume-title":"Computational Intelligence in Expensive Optimization Problems","author":"D Ginsbourger","year":"2010","unstructured":"Ginsbourger, D., Le Riche, R., Carraro, L.: Kriging is well-suited to parallelize optimization. In: Tenne, Y., Goh, C.-K. (eds.) Computational Intelligence in Expensive Optimization Problems. ALO, vol. 2, pp. 131\u2013162. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-10701-6_6"},{"issue":"1","key":"17_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00158-016-1432-3","volume":"54","author":"RT Haftka","year":"2016","unstructured":"Haftka, R.T., Villanueva, D., Chaudhuri, A.: Parallel surrogate-assisted global optimization with expensive functions-a survey. Struct. Multidiscip. Optim. 54(1), 3\u201313 (2016)","journal-title":"Struct. Multidiscip. Optim."},{"key":"17_CR12","unstructured":"Hansen, N., Auger, A., Mersmann, O., Tusar, T., Brockhoff, D.: COCO: a platform for comparing continuous optimizers in a black-box setting. arXiv e-prints, August 2016"},{"issue":"2","key":"17_CR13","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1162\/106365601750190398","volume":"9","author":"N Hansen","year":"2001","unstructured":"Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159\u2013195 (2001)","journal-title":"Evol. Comput."},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Hansen, N., Akimoto, Y., Baudis, P.: CMA-ES\/pycma on Github. Zenodo, February 2019. https:\/\/doi.org\/10.5281\/zenodo.2559634","DOI":"10.5281\/zenodo.2559634"},{"key":"17_CR15","doi-asserted-by":"publisher","unstructured":"Hansen, N., et al.: COmparing Continuous Optimizers: numbbo\/COCO on Github, March 2019. https:\/\/doi.org\/10.5281\/zenodo.2594848","DOI":"10.5281\/zenodo.2594848"},{"key":"17_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/978-3-319-77538-8_46","volume-title":"Applications of Evolutionary Computation","author":"E Hupkes","year":"2018","unstructured":"Hupkes, E., Jelisavcic, M., Eiben, A.E.: Revolve: a versatile simulator for online robot evolution. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 687\u2013702. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-77538-8_46"},{"key":"17_CR17","unstructured":"Nikolaus, H., Steffen, F., Raymond, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: noiseless functions definitions. Research Report INRIA - 00362633v2, INRIA (2009)"},{"key":"17_CR18","unstructured":"Pohlert, T.: The pairwise multiple comparison of mean ranks package (PMCMR) (2014). http:\/\/CRAN.R-project.org\/package=PMCMR . Accessed 12 Jan 2016"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Rasmussen, C., Williams, C.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2006)","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Rehbach, F., Zaefferer, M., Naujoks, B., Bartz-Beielstein, T.: Expected improvement versus predicted value in surrogate-based optimization (2020)","DOI":"10.1145\/3377930.3389816"},{"issue":"1","key":"17_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v051.i01","volume":"51","author":"O Roustant","year":"2012","unstructured":"Roustant, O., Ginsbourger, D., Deville, Y.: DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J. Stat. Softw. 51(1), 1\u201355 (2012)","journal-title":"J. Stat. Softw."},{"issue":"1","key":"17_CR22","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2016","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148\u2013175 (2016). https:\/\/doi.org\/10.1109\/JPROC.2015.2494218","journal-title":"Proc. IEEE"},{"issue":"5","key":"17_CR23","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1109\/tit.2011.2182033","volume":"58","author":"N Srinivas","year":"2012","unstructured":"Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Information-theoretic regret bounds for Gaussian process optimization in the bandit setting. IEEE Trans. Inf. Theory 58(5), 3250\u20133265 (2012). https:\/\/doi.org\/10.1109\/tit.2011.2182033","journal-title":"IEEE Trans. Inf. Theory"},{"key":"17_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1007\/978-3-319-10762-2_36","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XIII","author":"RK Ursem","year":"2014","unstructured":"Ursem, R.K.: From expected improvement to investment portfolio improvement: spreading the risk in kriging-based optimization. In: Bartz-Beielstein, T., Branke, J., Filipi\u010d, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 362\u2013372. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10762-2_36"},{"key":"17_CR25","unstructured":"Ushey, K., Allaire, J., Tang, Y.: Reticulate: Interface to \u2018Python\u2019 (2019). https:\/\/CRAN.R-project.org\/package=reticulate , r package version 1.13"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVI"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58112-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T02:32:16Z","timestamp":1723516336000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-58112-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030581114","9783030581121"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58112-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"31 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Leiden","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2020.liacs.leidenuniv.nl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"268","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":"99","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":"37% - 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":"3","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":"2.2","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)"}}]}}