{"id":"https://openalex.org/W2118658055","doi":"https://doi.org/10.1109/cec.2007.4424677","title":"Hybridisation of evolutionary programming and machine learning with k-nearest neighbor estimation","display_name":"Hybridisation of evolutionary programming and machine learning with k-nearest neighbor estimation","publication_year":2007,"publication_date":"2007-09-01","ids":{"openalex":"https://openalex.org/W2118658055","doi":"https://doi.org/10.1109/cec.2007.4424677","mag":"2118658055"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2007.4424677","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2007.4424677","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE Congress on Evolutionary Computation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5047287848","display_name":"Jingsong He","orcid":"https://orcid.org/0000-0002-2068-1849"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingsong He","raw_affiliation_strings":["Department of Electronic Science and Technology, USTC, China","Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, Anhui, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Science and Technology, USTC, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433323","display_name":"Zhenyu Yang","orcid":"https://orcid.org/0000-0002-0773-0298"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Yang","raw_affiliation_strings":["Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, Anhui, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100635494","display_name":"Xin Yao","orcid":"https://orcid.org/0000-0001-8837-4442"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yao","raw_affiliation_strings":["Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, Anhui, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nature Inspired Computation and Applications Laboratory, University of Science and Technology, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2226,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48354072,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1693","last_page":"1700"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9998999834060669,"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":0.9998999834060669,"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/T11975","display_name":"Evolutionary Algorithms and Applications","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":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/edas","display_name":"EDAS","score":0.9706878662109375},{"id":"https://openalex.org/keywords/estimation-of-distribution-algorithm","display_name":"Estimation of distribution algorithm","score":0.7969306111335754},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6623227000236511},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.655613362789154},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.6067430973052979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5838624835014343},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.5571884512901306},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.5493096113204956},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5030788779258728},{"id":"https://openalex.org/keywords/evolutionary-programming","display_name":"Evolutionary programming","score":0.5016446113586426},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"Evolutionary computation","score":0.4977248013019562},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4819040596485138},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.47104430198669434},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.44634199142456055},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.42488664388656616},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25685766339302063}],"concepts":[{"id":"https://openalex.org/C49284225","wikidata":"https://www.wikidata.org/wiki/Q5322829","display_name":"EDAS","level":3,"score":0.9706878662109375},{"id":"https://openalex.org/C162500139","wikidata":"https://www.wikidata.org/wiki/Q2835887","display_name":"Estimation of distribution algorithm","level":2,"score":0.7969306111335754},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6623227000236511},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.655613362789154},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.6067430973052979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5838624835014343},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.5571884512901306},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.5493096113204956},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5030788779258728},{"id":"https://openalex.org/C121835503","wikidata":"https://www.wikidata.org/wiki/Q2596288","display_name":"Evolutionary programming","level":3,"score":0.5016446113586426},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.4977248013019562},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4819040596485138},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.47104430198669434},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.44634199142456055},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42488664388656616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25685766339302063},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cec.2007.4424677","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2007.4424677","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE Congress on Evolutionary Computation","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.132.7520","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.132.7520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.bham.ac.uk/~xin/papers/HeYangYaoCEC07.pdf","raw_type":"text"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/ddbf1a72-7146-456d-846d-6ebad4f311d7","is_oa":false,"landing_page_url":"https://research.birmingham.ac.uk/portal/en/publications/hybridisation-of-evolutionary-programming-and-machine-learning-with-knearest-neighbor-estimation(ddbf1a72-7146-456d-846d-6ebad4f311d7).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"He, J, Yang, Z & Yao, X 2007, Hybridisation of Evolutionary Programming and Machine Learning with k-Nearest Neighbor Estimation. in IEEE Congress on Evolutionary Computation, 2007. CEC 2007.. Institute of Electrical and Electronics Engineers (IEEE), pp. 1693-1700, IEEE Congress on Evolutionary Computation, 2007 (CEC 2007), Singapore, Singapore, 25/09/07. https://doi.org/10.1109/CEC.2007.4424677","raw_type":"contributionToPeriodical"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W107095635","https://openalex.org/W145245653","https://openalex.org/W153832914","https://openalex.org/W1540706608","https://openalex.org/W1541774237","https://openalex.org/W1597531876","https://openalex.org/W1978970913","https://openalex.org/W1997600725","https://openalex.org/W2001100124","https://openalex.org/W2020009149","https://openalex.org/W2044026582","https://openalex.org/W2053559291","https://openalex.org/W2086717587","https://openalex.org/W2102660810","https://openalex.org/W2118020555","https://openalex.org/W2122111042","https://openalex.org/W2127817213","https://openalex.org/W2144636407","https://openalex.org/W2799061466","https://openalex.org/W3149025087","https://openalex.org/W4244494905","https://openalex.org/W6604462748","https://openalex.org/W6606290098"],"related_works":["https://openalex.org/W1982259447","https://openalex.org/W2496664933","https://openalex.org/W1503012231","https://openalex.org/W1058439126","https://openalex.org/W2360241151","https://openalex.org/W2547072840","https://openalex.org/W3149308343","https://openalex.org/W2091246427","https://openalex.org/W57385547","https://openalex.org/W1589480767"],"abstract_inverted_index":{"Evolutionary":[0],"programming":[1],"(EP)":[2],"focus":[3,33],"on":[4,34],"the":[5,11,21,36,54,87,112,117,121,142,155],"search":[6,24,51,101],"step":[7],"size":[8],"which":[9,125],"decides":[10],"ability":[12],"of":[13,23,29,46,57,85,89,133,144,157,165],"escaping":[14],"local":[15],"minima,":[16],"however":[17,40],"does":[18],"not":[19],"touch":[20],"issue":[22],"in":[25,49,116],"promising":[26,37],"region.":[27],"Estimation":[28],"distribution":[30],"algorithms":[31,160],"(EDAs)":[32],"where":[35],"region":[38],"is,":[39],"have":[41,139],"less":[42],"consideration":[43],"about":[44],"behavior":[45],"each":[47,71],"individual":[48],"solution":[50],"algorithms.":[52],"Since":[53],"basic":[55],"ideas":[56,88],"EP":[58,90],"and":[59,91],"EDAs":[60,92,105],"are":[61],"quite":[62],"different,":[63],"it":[64],"is":[65,120],"possible":[66],"to":[67,82],"make":[68,83],"them":[69],"reinforce":[70],"other.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76],"present":[77],"a":[78,95,162],"hybrid":[79],"evolutionary":[80,158],"framework":[81,119],"use":[84,107],"both":[86],"through":[93],"introducing":[94],"mini":[96],"estimation":[97,113,124],"operator":[98],"into":[99],"EP's":[100],"cycle.":[102],"Unlike":[103],"previous":[104],"that":[106,141],"probability":[108],"density":[109],"function":[110],"(PDF),":[111],"mechanism":[114],"used":[115],"proposed":[118],"k-nearest":[122,150],"neighbor":[123,151],"can":[126,153],"perform":[127],"better":[128],"with":[129],"relative":[130],"small":[131],"amount":[132],"training":[134],"samples.":[135],"Our":[136],"experimental":[137],"results":[138],"shown":[140],"incorporation":[143],"machine":[145],"learning":[146],"techniques,":[147],"such":[148],"as":[149],"estimation,":[152],"improve":[154],"performance":[156],"optimisation":[159],"for":[161],"large":[163],"number":[164],"benchmark":[166],"functions.":[167]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}