{"id":"https://openalex.org/W1670967200","doi":"https://doi.org/10.1109/tevc.2015.2449293","title":"A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization","display_name":"A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization","publication_year":2015,"publication_date":"2015-07-21","ids":{"openalex":"https://openalex.org/W1670967200","doi":"https://doi.org/10.1109/tevc.2015.2449293","mag":"1670967200"},"language":"en","primary_location":{"id":"doi:10.1109/tevc.2015.2449293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tevc.2015.2449293","pdf_url":null,"source":{"id":"https://openalex.org/S93787993","display_name":"IEEE Transactions on Evolutionary Computation","issn_l":"1089-778X","issn":["1089-778X","1941-0026"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Evolutionary Computation","raw_type":"journal-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/A5013105439","display_name":"Wenyin Gong","orcid":"https://orcid.org/0000-0003-1610-6865"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenyin Gong","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China","Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China#TAB#","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050248676","display_name":"Aimin Zhou","orcid":"https://orcid.org/0000-0002-4768-5946"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aimin Zhou","raw_affiliation_strings":["Department of Computer Science and Technology, East China Normal University, Shanghai, China","Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University  Shanghai China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University  Shanghai China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038861185","display_name":"Zhihua Cai","orcid":"https://orcid.org/0000-0003-0020-6503"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Cai","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China","Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China#TAB#","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013105439"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":21.4287,"has_fulltext":false,"cited_by_count":131,"citation_normalized_percentile":{"value":0.99409802,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"19","issue":"5","first_page":"746","last_page":"758"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9998000264167786,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.7084541320800781},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6596589088439941},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6181226372718811},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.5646079778671265},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5560179948806763},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"Evolutionary computation","score":0.5548299551010132},{"id":"https://openalex.org/keywords/test-suite","display_name":"Test suite","score":0.5278506278991699},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.5173146724700928},{"id":"https://openalex.org/keywords/differential-evolution","display_name":"Differential evolution","score":0.48593059182167053},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.485588014125824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4652109444141388},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3867270350456238},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25856927037239075},{"id":"https://openalex.org/keywords/test-case","display_name":"Test case","score":0.14946043491363525}],"concepts":[{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.7084541320800781},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6596589088439941},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6181226372718811},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5646079778671265},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5560179948806763},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.5548299551010132},{"id":"https://openalex.org/C151552104","wikidata":"https://www.wikidata.org/wiki/Q7705809","display_name":"Test suite","level":4,"score":0.5278506278991699},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.5173146724700928},{"id":"https://openalex.org/C74750220","wikidata":"https://www.wikidata.org/wiki/Q2662197","display_name":"Differential evolution","level":2,"score":0.48593059182167053},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.485588014125824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4652109444141388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3867270350456238},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25856927037239075},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.14946043491363525},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"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/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"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/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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tevc.2015.2449293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tevc.2015.2449293","pdf_url":null,"source":{"id":"https://openalex.org/S93787993","display_name":"IEEE Transactions on Evolutionary Computation","issn_l":"1089-778X","issn":["1089-778X","1941-0026"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Evolutionary Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3405310125","display_name":null,"funder_award_id":"61175063","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3423370395","display_name":null,"funder_award_id":"61203307","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4537160363","display_name":null,"funder_award_id":"61075063","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4760357681","display_name":null,"funder_award_id":"14DZ2260800","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"},{"id":"https://openalex.org/G7201549544","display_name":null,"funder_award_id":"61273313","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W6625593","https://openalex.org/W1500820894","https://openalex.org/W1551251808","https://openalex.org/W1559956479","https://openalex.org/W1595159159","https://openalex.org/W1970311014","https://openalex.org/W1973537145","https://openalex.org/W1976637668","https://openalex.org/W1976956135","https://openalex.org/W1978161072","https://openalex.org/W1986073756","https://openalex.org/W1989682464","https://openalex.org/W1997600725","https://openalex.org/W1998391576","https://openalex.org/W2000503034","https://openalex.org/W2005383742","https://openalex.org/W2011174137","https://openalex.org/W2012451526","https://openalex.org/W2016114852","https://openalex.org/W2017816236","https://openalex.org/W2020320008","https://openalex.org/W2028031385","https://openalex.org/W2041946090","https://openalex.org/W2045476276","https://openalex.org/W2052367557","https://openalex.org/W2059836574","https://openalex.org/W2079754109","https://openalex.org/W2082228576","https://openalex.org/W2083281224","https://openalex.org/W2096369439","https://openalex.org/W2098540440","https://openalex.org/W2100274914","https://openalex.org/W2101473977","https://openalex.org/W2104274529","https://openalex.org/W2105245738","https://openalex.org/W2109116383","https://openalex.org/W2111699129","https://openalex.org/W2112844689","https://openalex.org/W2114620686","https://openalex.org/W2115343860","https://openalex.org/W2117250519","https://openalex.org/W2120138297","https://openalex.org/W2123682012","https://openalex.org/W2124548165","https://openalex.org/W2132181343","https://openalex.org/W2135366549","https://openalex.org/W2137295153","https://openalex.org/W2137340504","https://openalex.org/W2144051602","https://openalex.org/W2146713522","https://openalex.org/W2146879413","https://openalex.org/W2152195021","https://openalex.org/W2154047522","https://openalex.org/W2155529731","https://openalex.org/W2156773695","https://openalex.org/W2157557172","https://openalex.org/W2162145193","https://openalex.org/W2169284845","https://openalex.org/W2502271158","https://openalex.org/W3034562304","https://openalex.org/W4239875977","https://openalex.org/W6632908148","https://openalex.org/W6642760193"],"related_works":["https://openalex.org/W2394392475","https://openalex.org/W2079483465","https://openalex.org/W2069937987","https://openalex.org/W2370079837","https://openalex.org/W4385188307","https://openalex.org/W2107894985","https://openalex.org/W2726178318","https://openalex.org/W2801693842","https://openalex.org/W2015132550","https://openalex.org/W1966154590"],"abstract_inverted_index":{"It":[0],"is":[1,103,136,148],"well":[2],"known":[3],"that":[4,141,158,190],"in":[5,19,61,142,154,173,202],"evolutionary":[6,74],"algorithms":[7],"(EAs),":[8],"different":[9,16,20],"reproduction":[10,92],"operators":[11,32],"may":[12],"be":[13],"suitable":[14],"for":[15,73,123],"problems":[17],"or":[18],"running":[21],"stages.":[22],"To":[23],"improve":[24,195],"the":[25,28,52,89,95,100,106,127,132,143,170,174,180,191,196,203,206],"algorithm":[26],"performance,":[27],"ensemble":[29,37,59,153],"of":[30,81,112,198,205],"multiple":[31,90],"has":[33],"become":[34],"popular.":[35],"Most":[36],"techniques":[38],"achieve":[39],"this":[40,62,113],"goal":[41],"by":[42,87],"choosing":[43],"an":[44],"operator":[45,118],"according":[46,98],"to":[47,57,99,126,140,150,183],"a":[48,66,79,121],"probability":[49],"learned":[50],"from":[51],"previous":[53],"experience.":[54],"In":[55,76],"contrast":[56],"these":[58],"techniques,":[60],"paper":[63],"we":[64],"propose":[65],"cheap":[67,138],"surrogate":[68,101,133],"model-based":[69],"multioperator":[70,152],"search":[71],"strategy":[72],"optimization.":[75,165],"our":[77,185],"approach,":[78],"set":[80],"candidate":[82],"offspring":[83,91,107],"solutions":[84],"are":[85,177],"generated":[86],"using":[88],"operators,":[93],"and":[94,130,162,169],"best":[96],"one":[97],"model":[102,134,147],"chosen":[104,178],"as":[105,179],"solution.":[108],"Two":[109],"major":[110],"advantages":[111],"approach":[114,193],"are:":[115],"1)":[116],"each":[117],"can":[119,194],"generate":[120],"solution":[122],"competition":[124],"compared":[125,139],"probability-based":[128],"approaches":[129],"2)":[131],"building":[135],"relatively":[137],"surrogate-assisted":[144],"EAs.":[145],"The":[146],"used":[149],"implement":[151],"two":[155],"popular":[156],"EAs,":[157],"is,":[159],"differential":[160],"evolution":[161],"particle":[163],"swarm":[164],"Thirty":[166],"benchmark":[167],"functions":[168,171],"presented":[172],"CEC":[175],"2013":[176],"test":[181],"suite":[182],"evaluate":[184],"approach.":[186],"Experimental":[187],"results":[188],"indicate":[189],"new":[192],"performance":[197],"single":[199],"operator-based":[200],"methods":[201],"majority":[204],"functions.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":16},{"year":2017,"cited_by_count":21},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
