{"id":"https://openalex.org/W2063075776","doi":"https://doi.org/10.1145/2739480.2754704","title":"Sample Reuse in the Covariance Matrix Adaptation Evolution Strategy Based on Importance Sampling","display_name":"Sample Reuse in the Covariance Matrix Adaptation Evolution Strategy Based on Importance Sampling","publication_year":2015,"publication_date":"2015-07-07","ids":{"openalex":"https://openalex.org/W2063075776","doi":"https://doi.org/10.1145/2739480.2754704","mag":"2063075776"},"language":"en","primary_location":{"id":"doi:10.1145/2739480.2754704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2739480.2754704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005268349","display_name":"Shinichi Shirakawa","orcid":"https://orcid.org/0000-0002-4659-6108"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Shinichi Shirakawa","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Ibaraki, Japan","University of Tsukuba, Tsukuba , Ibaraki , Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Ibaraki, Japan","institution_ids":["https://openalex.org/I146399215"]},{"raw_affiliation_string":"University of Tsukuba, Tsukuba , Ibaraki , Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038757231","display_name":"Youhei Akimoto","orcid":"https://orcid.org/0000-0003-2760-8123"},"institutions":[{"id":"https://openalex.org/I4210162928","display_name":"\u014ctani University","ror":"https://ror.org/05b7rex33","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210162928"]},{"id":"https://openalex.org/I126592722","display_name":"Nagano University","ror":"https://ror.org/03aptyv62","country_code":"JP","type":"education","lineage":["https://openalex.org/I126592722"]},{"id":"https://openalex.org/I137975476","display_name":"Shinshu University","ror":"https://ror.org/0244rem06","country_code":"JP","type":"education","lineage":["https://openalex.org/I137975476"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Youhei Akimoto","raw_affiliation_strings":["Shinshu University, Nagano City, Nagano, Japan","Shinshu University, Nagano City, Nagano, Japan#TAB#"],"affiliations":[{"raw_affiliation_string":"Shinshu University, Nagano City, Nagano, Japan","institution_ids":["https://openalex.org/I126592722","https://openalex.org/I137975476","https://openalex.org/I4210162928"]},{"raw_affiliation_string":"Shinshu University, Nagano City, Nagano, Japan#TAB#","institution_ids":["https://openalex.org/I126592722","https://openalex.org/I137975476","https://openalex.org/I4210162928"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011404442","display_name":"Kazuki Ouchi","orcid":null},"institutions":[{"id":"https://openalex.org/I131231118","display_name":"Aoyama Gakuin University","ror":"https://ror.org/002rw7y37","country_code":"JP","type":"education","lineage":["https://openalex.org/I131231118"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuki Ouchi","raw_affiliation_strings":["Aoyama Gakuin University, Sagamihara-shi, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"Aoyama Gakuin University, Sagamihara-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I131231118"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031196637","display_name":"Kouzou Ohara","orcid":"https://orcid.org/0000-0002-7399-2472"},"institutions":[{"id":"https://openalex.org/I131231118","display_name":"Aoyama Gakuin University","ror":"https://ror.org/002rw7y37","country_code":"JP","type":"education","lineage":["https://openalex.org/I131231118"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kouzou Ohara","raw_affiliation_strings":["Aoyama Gakuin University, Sagamihara-shi, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"Aoyama Gakuin University, Sagamihara-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I131231118"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5005268349"],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":0.5743,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66824092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"305","last_page":"312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cma-es","display_name":"CMA-ES","score":0.8238002061843872},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6110560894012451},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.6024291515350342},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6022041440010071},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.537365734577179},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5254225730895996},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5156861543655396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4813871681690216},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47162532806396484},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.47161605954170227},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.45448920130729675},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4273712933063507},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4212809205055237},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.400636225938797},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.39786073565483093},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3737986087799072}],"concepts":[{"id":"https://openalex.org/C205555498","wikidata":"https://www.wikidata.org/wiki/Q505588","display_name":"CMA-ES","level":4,"score":0.8238002061843872},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6110560894012451},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.6024291515350342},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6022041440010071},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.537365734577179},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5254225730895996},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5156861543655396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4813871681690216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47162532806396484},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.47161605954170227},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.45448920130729675},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4273712933063507},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4212809205055237},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.400636225938797},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.39786073565483093},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3737986087799072},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2739480.2754704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2739480.2754704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W7934506","https://openalex.org/W102487131","https://openalex.org/W1480347379","https://openalex.org/W1579744901","https://openalex.org/W1687697105","https://openalex.org/W1880549478","https://openalex.org/W1970789124","https://openalex.org/W1998510482","https://openalex.org/W2090346540","https://openalex.org/W2112036188","https://openalex.org/W2130990842","https://openalex.org/W2136495462","https://openalex.org/W2142508340","https://openalex.org/W2151965738","https://openalex.org/W2208154600"],"related_works":["https://openalex.org/W2926551842","https://openalex.org/W3119219900","https://openalex.org/W2022594112","https://openalex.org/W1983957588","https://openalex.org/W2258123186","https://openalex.org/W2572601863","https://openalex.org/W3112846993","https://openalex.org/W4311761947","https://openalex.org/W1626396758","https://openalex.org/W2554071524"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"reveal":[2],"that":[3,130],"the":[4,12,16,24,27,31,35,40,43,50,54,67,70,73,76,83,87,90,95,98,102,106,111,118,121,125,131,137,148],"covariance":[5,103],"matrix":[6,104],"adaptation":[7],"evolution":[8],"strategy":[9],"(CMA-ES)":[10],"updates":[11],"parameters":[13,36],"based":[14],"on":[15,142],"natural":[17,44,91],"gradient.":[18,92],"The":[19],"rank-based":[20],"weight":[21],"is":[22,155],"considered":[23],"result":[25],"of":[26,30,42,72,86,101,139,150],"quantile-based":[28],"transformation":[29],"objective":[32],"value":[33],"and":[34,89,105],"are":[37,79],"adjusted":[38],"in":[39],"direction":[41],"gradient":[45],"estimated":[46],"by":[47],"Monte-Carlo":[48],"with":[49],"samples":[51,78,151],"drawn":[52],"from":[53],"current":[55],"distribution.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60,128],"propose":[61],"a":[62],"sample":[63],"reuse":[64],"mechanism":[65],"for":[66,97],"CMA-ES.":[68],"On":[69],"basis":[71],"importance":[74],"sampling,":[75],"past":[77,112],"reused":[80],"to":[81,135],"reduce":[82,136],"estimation":[84],"variance":[85],"quantile":[88],"We":[93],"derive":[94],"formula":[96],"rank-\u00a5mu":[99],"update":[100,109],"mean":[107],"vector":[108],"using":[110],"samples,":[113],"then":[114],"incorporate":[115],"it":[116],"into":[117],"CMA-ES":[119],"without":[120],"step-size":[122],"adaptation.":[123],"From":[124],"numerical":[126],"experiments,":[127],"observe":[129],"proposed":[132],"approach":[133],"helps":[134],"number":[138,149],"function":[140],"evaluations":[141],"many":[143],"benchmark":[144],"functions,":[145],"especially":[146],"when":[147],"at":[152],"each":[153],"iteration":[154],"relatively":[156],"small.":[157]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
