{"id":"https://openalex.org/W1592902649","doi":"https://doi.org/10.1109/cec.2004.1330830","title":"Neural network regularization and ensembling using multi-objective evolutionary algorithms","display_name":"Neural network regularization and ensembling using multi-objective evolutionary algorithms","publication_year":2004,"publication_date":"2004-09-28","ids":{"openalex":"https://openalex.org/W1592902649","doi":"https://doi.org/10.1109/cec.2004.1330830","mag":"1592902649"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2004.1330830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2004.1330830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","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/A5032314861","display_name":"Yaochu Jin","orcid":"https://orcid.org/0000-0003-1100-0631"},"institutions":[{"id":"https://openalex.org/I1283473643","display_name":"Honda (Japan)","ror":"https://ror.org/03jzay846","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283473643"]},{"id":"https://openalex.org/I4210112253","display_name":"Honda (Germany)","ror":"https://ror.org/022c1xk47","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"]}],"countries":["DE","JP"],"is_corresponding":true,"raw_author_name":"Yaochu Jin","raw_affiliation_strings":["Honda Research Institute Europe GmbH, Offenbach, Germany","[Honda Res. Inst. Eur., Offenbach, Germany]"],"affiliations":[{"raw_affiliation_string":"Honda Research Institute Europe GmbH, Offenbach, Germany","institution_ids":["https://openalex.org/I4210112253"]},{"raw_affiliation_string":"[Honda Res. Inst. Eur., Offenbach, Germany]","institution_ids":["https://openalex.org/I1283473643"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033637736","display_name":"Tatsuya Okabe","orcid":null},"institutions":[{"id":"https://openalex.org/I4210112253","display_name":"Honda (Germany)","ror":"https://ror.org/022c1xk47","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"T. Okabe","raw_affiliation_strings":["Honda Research Institute Europe GmbH, Offenbach, Germany","HONDA Research Institute Europe GmbH, Offenbach, Germany"],"affiliations":[{"raw_affiliation_string":"Honda Research Institute Europe GmbH, Offenbach, Germany","institution_ids":["https://openalex.org/I4210112253"]},{"raw_affiliation_string":"HONDA Research Institute Europe GmbH, Offenbach, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066418464","display_name":"Bernhard Sendhoff","orcid":"https://orcid.org/0000-0002-1233-9584"},"institutions":[{"id":"https://openalex.org/I4210112253","display_name":"Honda (Germany)","ror":"https://ror.org/022c1xk47","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"B. Sendhoff","raw_affiliation_strings":["Honda Research Institute Europe GmbH, Offenbach, Germany","HONDA Research Institute Europe GmbH, Offenbach, Germany"],"affiliations":[{"raw_affiliation_string":"Honda Research Institute Europe GmbH, Offenbach, Germany","institution_ids":["https://openalex.org/I4210112253"]},{"raw_affiliation_string":"HONDA Research Institute Europe GmbH, Offenbach, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032314861"],"corresponding_institution_ids":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"],"apc_list":null,"apc_paid":null,"fwci":13.8168,"has_fulltext":false,"cited_by_count":141,"citation_normalized_percentile":{"value":0.99245893,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9991000294685364,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9961000084877014,"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/T10320","display_name":"Neural Networks and Applications","score":0.9936000108718872,"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/regularization","display_name":"Regularization (linguistics)","score":0.67154860496521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6155821681022644},{"id":"https://openalex.org/keywords/proximal-gradient-methods-for-learning","display_name":"Proximal gradient methods for learning","score":0.6079521179199219},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.604266881942749},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5495237708091736},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5000593662261963},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.47205883264541626},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.46013468503952026},{"id":"https://openalex.org/keywords/backus\u2013gilbert-method","display_name":"Backus\u2013Gilbert method","score":0.4234587252140045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41763466596603394},{"id":"https://openalex.org/keywords/regularization-perspectives-on-support-vector-machines","display_name":"Regularization perspectives on support vector machines","score":0.3805314302444458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3265620470046997},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3028501868247986},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.252447247505188},{"id":"https://openalex.org/keywords/tikhonov-regularization","display_name":"Tikhonov regularization","score":0.08901527523994446}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.67154860496521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6155821681022644},{"id":"https://openalex.org/C79248915","wikidata":"https://www.wikidata.org/wiki/Q17086776","display_name":"Proximal gradient methods for learning","level":5,"score":0.6079521179199219},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.604266881942749},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5495237708091736},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5000593662261963},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.47205883264541626},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.46013468503952026},{"id":"https://openalex.org/C27872270","wikidata":"https://www.wikidata.org/wiki/Q4839810","display_name":"Backus\u2013Gilbert method","level":5,"score":0.4234587252140045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41763466596603394},{"id":"https://openalex.org/C141718189","wikidata":"https://www.wikidata.org/wiki/Q7309628","display_name":"Regularization perspectives on support vector machines","level":4,"score":0.3805314302444458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3265620470046997},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3028501868247986},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.252447247505188},{"id":"https://openalex.org/C152442038","wikidata":"https://www.wikidata.org/wiki/Q2778212","display_name":"Tikhonov regularization","level":3,"score":0.08901527523994446},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/cec.2004.1330830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2004.1330830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","raw_type":"proceedings-article"},{"id":"pmh:oai:alma.44SUR_INST:11139052210002346","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4210197018","display_name":"View","issn_l":"2688-268X","issn":["2688-268X","2688-3988"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.74.2907","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.2907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.lania.mx/~ccoello/EMOO/jin04a.pdf.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.91.8484","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.8484","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.soft-computing.de/YJin_CEC04.pdf","raw_type":"text"},{"id":"pmh:oai:epubs.surrey.ac.uk:532846","is_oa":false,"landing_page_url":"http://epubs.surrey.ac.uk/532846/1/jin04a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400680","display_name":"Surrey Research Insight Open Access (The University of Surrey)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28290843","host_organization_name":"University of Surrey","host_organization_lineage":["https://openalex.org/I28290843"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W197326900","https://openalex.org/W264036554","https://openalex.org/W1507599800","https://openalex.org/W1520622896","https://openalex.org/W1554663460","https://openalex.org/W1587065653","https://openalex.org/W1622484071","https://openalex.org/W1859045945","https://openalex.org/W2015437745","https://openalex.org/W2035042171","https://openalex.org/W2044861997","https://openalex.org/W2081808304","https://openalex.org/W2097554207","https://openalex.org/W2106390255","https://openalex.org/W2145833756","https://openalex.org/W2146300243","https://openalex.org/W2159329525","https://openalex.org/W2161205534","https://openalex.org/W2164544703","https://openalex.org/W2166739626","https://openalex.org/W2167159964","https://openalex.org/W2327088997","https://openalex.org/W4241304137","https://openalex.org/W4244471710","https://openalex.org/W4388297464","https://openalex.org/W6631016846","https://openalex.org/W6674406337","https://openalex.org/W6684212943","https://openalex.org/W6684636516"],"related_works":["https://openalex.org/W2366023887","https://openalex.org/W1481686068","https://openalex.org/W1989857057","https://openalex.org/W2034445081","https://openalex.org/W3030705903","https://openalex.org/W1603753160","https://openalex.org/W4318820823","https://openalex.org/W2520385699","https://openalex.org/W2080188834","https://openalex.org/W2062460234"],"abstract_inverted_index":{"Regularization":[0],"is":[1,13,35,162],"an":[2,17,153],"essential":[3],"technique":[4],"to":[5,41,114,124,165,178,228],"improve":[6],"generalization":[7],"of":[8,24,31,67,76,87,121,131,136,147,173,196,203,213,232],"neural":[9,58,78,115,179,182],"networks.":[10],"Traditionally,":[11],"regularization":[12,33,45,60,117,156],"conducted":[14],"by":[15,209],"including":[16],"additional":[18],"term":[19,157],"in":[20,142,217],"the":[21,44,47,57,70,77,92,99,125,174,190,200,204,211,218,230,233],"cost":[22],"function":[23,227],"a":[25,37,63,119,129,134,170,225],"learning":[26,48,167],"algorithm.":[27],"One":[28],"main":[29],"drawback":[30],"these":[32],"techniques":[34],"that":[36,39],"hyperparameter":[38],"determines":[40],"which":[42,161],"extension":[43],"influences":[46],"algorithm":[49,104],"must":[50],"be":[51,81,140,159,186,207],"determined":[52],"beforehand.":[53],"This":[54],"paper":[55],"addresses":[56],"network":[59,79,116,180,183,216],"problem":[61],"from":[62],"multi-objective":[64,89,112,175],"optimization":[65,90,144,176],"point":[66],"view.":[68],"During":[69],"optimization,":[71],"both":[72],"structure":[73],"and":[74,98,108],"parameters":[75],"will":[80],"optimized.":[82],"A":[83],"slightly":[84],"modified":[85],"version":[86],"two":[88],"algorithms,":[91],"dynamic":[93],"weighted":[94],"aggregation":[95],"(DWA)":[96],"method":[97],"elitist":[100],"non-dominated":[101],"sorting":[102],"genetic":[103],"(NSGA-II)":[105],"are":[106,221],"used":[107],"compared.":[109],"An":[110],"evolutionary":[111],"approach":[113,177],"has":[118],"number":[120,130],"advantages":[122],"compared":[123],"traditional":[126],"methods.":[127],"First,":[128],"models":[132],"with":[133,193],"spectrum":[135],"model":[137,197,201],"complexity":[138,202],"can":[139,158,185,206],"obtained":[141,191],"one":[143,149],"run":[145],"instead":[146],"only":[148],"single":[150],"solution.":[151],"Second,":[152],"efficient":[154],"new":[155],"introduced,":[160],"not":[163],"applicable":[164],"gradient-based":[166],"algorithms.":[168],"As":[169],"natural":[171],"by-product":[172],"regularization,":[181],"ensembles":[184],"easily":[187],"constructed":[188],"using":[189],"networks":[192],"different":[194],"levels":[195],"complexity.":[198],"Thus,":[199],"ensemble":[205],"adjusted":[208],"adjusting":[210],"weight":[212],"each":[214],"member":[215],"ensemble.":[219],"Simulations":[220],"carried":[222],"out":[223],"on":[224],"test":[226],"illustrate":[229],"feasibility":[231],"proposed":[234],"ideas.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
