{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:29:54Z","timestamp":1750220994189,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,13]],"date-time":"2019-07-13T00:00:00Z","timestamp":1562976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"\u00f0esk\u00e9 Vysok\u00e9 U\u00f0en\u00ed Technick\u00e9 v Praze","award":["SGS17\/193\/OHK4\/3T\/14"],"award-info":[{"award-number":["SGS17\/193\/OHK4\/3T\/14"]}]},{"name":"Grantov\u00e1 Agentura \u00f0esk\u00e9 Republiky","award":["17-01251"],"award-info":[{"award-number":["17-01251"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,13]]},"DOI":"10.1145\/3321707.3321861","type":"proceedings-article","created":{"date-parts":[[2019,7,3]],"date-time":"2019-07-03T13:48:04Z","timestamp":1562161684000},"page":"691-699","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy"],"prefix":"10.1145","author":[{"given":"Zbyn\u011bk","family":"Pitra","sequence":"first","affiliation":[{"name":"Czech Technical University, Prague, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jakub","family":"Repick\u00fd","sequence":"additional","affiliation":[{"name":"Charles University, Prague, Czech Rep."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Hole\u0148a","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Prague, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,7,13]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2005.1554902"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"A. Auger M. Schoenauer and N. Vanhaecke. 2004. LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation. In Parallel Problem Solving from Nature - PPSN VIII. 182--191. A. Auger M. Schoenauer and N. Vanhaecke. 2004. LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation. In Parallel Problem Solving from Nature - PPSN VIII. 182--191.","DOI":"10.1007\/978-3-540-30217-9_19"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"M. Baerns and M. Hole\u0148a. 2009. Combinatorial Development of Solid Catalytic Materials. Design of High-Throughput Experiments Data Analysis Data Mining. M. Baerns and M. Hole\u0148a. 2009. Combinatorial Development of Solid Catalytic Materials. Design of High-Throughput Experiments Data Analysis Data Mining.","DOI":"10.1142\/p620"},{"key":"e_1_3_2_2_4_1","unstructured":"L. Bajer Z. Pitra J. Repick\u00fd and M. Hole\u0148a. 0. Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evolutionary Computation 0 ja (0) 1--30. PMID: 30540493. L. Bajer Z. Pitra J. Repick\u00fd and M. Hole\u0148a. 0. Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evolutionary Computation 0 ja (0) 1--30. PMID: 30540493."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"N. Belkhir J. Dr\u00e9o P. Sav\u00e9ant and M. Schoenauer. 2016. Surrogate Assisted Feature Computation for Continuous Problems. In Learning and Intelligent Optimization. Springer International Publishing Cham 17--31. N. Belkhir J. Dr\u00e9o P. Sav\u00e9ant and M. Schoenauer. 2016. Surrogate Assisted Feature Computation for Continuous Problems. In Learning and Intelligent Optimization. Springer International Publishing Cham 17--31.","DOI":"10.1007\/978-3-319-50349-3_2"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Y. Bengio and Y. Lecun. 2007. Scaling learning algorithms towards AI. MIT Press. Y. Bengio and Y. Lecun. 2007. Scaling learning algorithms towards AI. MIT Press.","DOI":"10.7551\/mitpress\/7496.003.0016"},{"volume-title":"Classification and regression trees","author":"Breiman L.","key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","DOI":"10.1201\/9781315139470"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2004.841917"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248548"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2008.11.001"},{"volume-title":"Towards a New Evolutionary Computation. Number 192 in Studies in Fuzziness and Soft Computing","author":"Hansen N.","key":"e_1_3_2_2_12_1"},{"key":"e_1_3_2_2_13_1","unstructured":"N. Hansen A. Auger S. Finck and R. Ros. 2012. Real-Parameter Black-Box Optimization Benchmarking 2012: Experimental Setup. Technical Report. INRIA. N. Hansen A. Auger S. Finck and R. Ros. 2012. Real-Parameter Black-Box Optimization Benchmarking 2012: Experimental Setup. Technical Report. INRIA."},{"volume-title":"Technical Report RR-6829. INRIA. Updated","year":"2009","author":"Hansen N.","key":"e_1_3_2_2_14_1"},{"volume-title":"Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation. In Proceedings of IEEE International Conference on Evolutionary Computation","year":"1996","author":"Hansen N.","key":"e_1_3_2_2_15_1"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1830761.1830788"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11081-008-9037-3"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.05.001"},{"volume":"1","volume-title":"Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)","author":"Jin Y.","key":"e_1_3_2_2_19_1"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/11844297_95"},{"volume-title":"Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco. ArXiv e-prints","year":"2017","author":"Kerschke P.","key":"e_1_3_2_2_21_1"},{"key":"e_1_3_2_2_22_1","unstructured":"P. Kerschke and J. Dagefoerde. 2017. flacco: Feature-Based Landscape Analysis of Continuous and Constraint Optimization Problems. https:\/\/cran.r-project.org\/package=flacco R-package v. 1.7. P. Kerschke and J. Dagefoerde. 2017. flacco: Feature-Based Landscape Analysis of Continuous and Constraint Optimization Problems. https:\/\/cran.r-project.org\/package=flacco R-package v. 1.7."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2739480.2754642"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2016.05.051"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143997.1144085"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2001576.2001690"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2014.2302006"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.05.010"},{"volume-title":"Proceedings of the PPSN XIV: 14th International Conference","author":"Pitra Z.","key":"e_1_3_2_2_29_1"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3067695.3082539"},{"volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen C. E.","key":"e_1_3_2_2_31_1"},{"volume":"2192","volume-title":"Proceedings of the Workshop on IAL co-located with ECML PKDD 2018, Dublin, Ireland, September 10th, 2018. (CEUR Workshop Proceedings)","author":"Repick\u00fd J.","key":"e_1_3_2_2_32_1"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2017.8285281"},{"volume":"1","volume-title":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03","author":"Ulmer H.","key":"e_1_3_2_2_34_1"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.05.047"}],"event":{"name":"GECCO '19: Genetic and Evolutionary Computation Conference","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"Prague Czech Republic","acronym":"GECCO '19"},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3321707.3321861","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3321707.3321861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:29Z","timestamp":1750206329000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3321707.3321861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,13]]},"references-count":34,"alternative-id":["10.1145\/3321707.3321861","10.1145\/3321707"],"URL":"https:\/\/doi.org\/10.1145\/3321707.3321861","relation":{},"subject":[],"published":{"date-parts":[[2019,7,13]]},"assertion":[{"value":"2019-07-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}