{"id":"https://openalex.org/W2954532028","doi":"https://doi.org/10.1145/3321707.3321760","title":"Convolutional neural network surrogate-assisted GOMEA","display_name":"Convolutional neural network surrogate-assisted GOMEA","publication_year":2019,"publication_date":"2019-07-03","ids":{"openalex":"https://openalex.org/W2954532028","doi":"https://doi.org/10.1145/3321707.3321760","mag":"2954532028"},"language":"en","primary_location":{"id":"doi:10.1145/3321707.3321760","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3321707.3321760","pdf_url":"https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3321707.3321760&file=p753-dushatskiy_suppl.pdf&download=true","source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3321707.3321760&file=p753-dushatskiy_suppl.pdf&download=true","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006864822","display_name":"Arkadiy Dushatskiy","orcid":"https://orcid.org/0000-0003-0945-0262"},"institutions":[{"id":"https://openalex.org/I1341640284","display_name":"Centrum Wiskunde & Informatica","ror":"https://ror.org/00x7ekv49","country_code":"NL","type":"facility","lineage":["https://openalex.org/I1341640284","https://openalex.org/I2800991832","https://openalex.org/I4405262988"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Arkadiy Dushatskiy","raw_affiliation_strings":["Centrum Wiskunde &amp; Informatica, Amsterdam, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centrum Wiskunde &amp; Informatica, Amsterdam, the Netherlands","institution_ids":["https://openalex.org/I1341640284"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015319043","display_name":"Adri\u00ebnne M. Mendrik","orcid":"https://orcid.org/0000-0001-6631-7068"},"institutions":[{"id":"https://openalex.org/I4210095242","display_name":"Netherlands eScience Center","ror":"https://ror.org/00rbjv475","country_code":"NL","type":"funder","lineage":["https://openalex.org/I2800991832","https://openalex.org/I4210090210","https://openalex.org/I4210095242"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Adri\u00ebnne M. Mendrik","raw_affiliation_strings":["Netherlands eScience Center, Amsterdam, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Netherlands eScience Center, Amsterdam, the Netherlands","institution_ids":["https://openalex.org/I4210095242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025122230","display_name":"Tanja Alderliesten","orcid":"https://orcid.org/0000-0003-4261-7511"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Tanja Alderliesten","raw_affiliation_strings":["University of Amsterdam, Amsterdam, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, the Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031539135","display_name":"Peter A. N. Bosman","orcid":"https://orcid.org/0000-0002-4186-6666"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter A. N. Bosman","raw_affiliation_strings":["Delft University of Technology, Delft, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Delft, the Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.1286,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.92405289,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"753","last_page":"761"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9984999895095825,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9984999895095825,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.6742425560951233},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6548572182655334},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.651360034942627},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.6236549019813538},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6023012399673462},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5898822546005249},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5734950304031372},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.567999541759491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.508049726486206},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4877801537513733},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4641163945198059},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4496699273586273},{"id":"https://openalex.org/keywords/fitness-approximation","display_name":"Fitness approximation","score":0.43008798360824585},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41408300399780273},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33833861351013184},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.23621222376823425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22650617361068726}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6742425560951233},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6548572182655334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.651360034942627},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.6236549019813538},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6023012399673462},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5898822546005249},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5734950304031372},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.567999541759491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508049726486206},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4877801537513733},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4641163945198059},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4496699273586273},{"id":"https://openalex.org/C148392497","wikidata":"https://www.wikidata.org/wiki/Q16250539","display_name":"Fitness approximation","level":4,"score":0.43008798360824585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41408300399780273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33833861351013184},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.23621222376823425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22650617361068726},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3321707.3321760","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3321707.3321760","pdf_url":"https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3321707.3321760&file=p753-dushatskiy_suppl.pdf&download=true","source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.amsterdamumc.nl:publications/7d77886e-b0c2-457c-ae27-3d017a90dedf","is_oa":false,"landing_page_url":"https://pure.amsterdamumc.nl/en/publications/7d77886e-b0c2-457c-ae27-3d017a90dedf","pdf_url":null,"source":{"id":"https://openalex.org/S7407055222","display_name":"Pure Amsterdam UMC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Dushatskiy, A, Alderliesten, T, Mendrik, A M & Bosman, P A N 2019, Convolutional neural network surrogate-assisted GOMEA. in GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, pp. 753-761, 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 13/07/2019. https://doi.org/10.1145/3321707.3321760","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:cwi.nl:28897","is_oa":true,"landing_page_url":"https://ir.cwi.nl/pub/28897","pdf_url":"https://ir.cwi.nl/pub/28897/28897.pdf","source":{"id":"https://openalex.org/S7407055335","display_name":"Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:amcpub:oai:pure.amc.nl:publications/b4abae71-0573-49b4-9f2b-56525a946739","is_oa":false,"landing_page_url":"https://pure.amc.nl/en/publications/convolutional-neural-network-surrogateassisted-gomea(b4abae71-0573-49b4-9f2b-56525a946739).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 753 - 761","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":{"id":"doi:10.1145/3321707.3321760","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3321707.3321760","pdf_url":"https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3321707.3321760&file=p753-dushatskiy_suppl.pdf&download=true","source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6216667842","display_name":"FEDMix: Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis","funder_award_id":"628.011.012","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2954532028.pdf","grobid_xml":"https://content.openalex.org/works/W2954532028.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W861752098","https://openalex.org/W1437335841","https://openalex.org/W1547531277","https://openalex.org/W1566613813","https://openalex.org/W1928278792","https://openalex.org/W1963955294","https://openalex.org/W1972978214","https://openalex.org/W1994197834","https://openalex.org/W2032602526","https://openalex.org/W2047284461","https://openalex.org/W2092076923","https://openalex.org/W2095705004","https://openalex.org/W2106411961","https://openalex.org/W2116926066","https://openalex.org/W2143192733","https://openalex.org/W2192203593","https://openalex.org/W2419109832","https://openalex.org/W2557487042","https://openalex.org/W2586846315","https://openalex.org/W2790436084","https://openalex.org/W2793997912","https://openalex.org/W2888618521","https://openalex.org/W2963840672"],"related_works":["https://openalex.org/W4380627621","https://openalex.org/W4292081304","https://openalex.org/W2950792054","https://openalex.org/W3187720583","https://openalex.org/W4237912051","https://openalex.org/W4312935382","https://openalex.org/W1518025915","https://openalex.org/W4225159429","https://openalex.org/W2891149252","https://openalex.org/W2102251959"],"abstract_inverted_index":{"We":[0,50],"introduce":[1],"a":[2,30,41,47,78],"novel":[3],"surrogate-assisted":[4],"Genetic":[5],"Algorithm":[6,28],"(GA)":[7],"for":[8,34,157],"expensive":[9,116],"optimization":[10,117],"of":[11,22,64,80,100,124,127,166],"problems":[12,141],"with":[13,110,142],"discrete":[14],"categorical":[15],"variables.":[16,162],"Specifically,":[17],"we":[18,82],"leverage":[19],"the":[20,23,35,54,62,113,125,134,150,164,174],"strengths":[21],"Gene-pool":[24],"Optimal":[25],"Mixing":[26],"Evolutionary":[27],"(GOMEA),":[29],"state-of-the-art":[31],"GA,":[32],"and,":[33],"first":[36],"time,":[37],"propose":[38,51],"to":[39,52,60,70,95,132,172,178],"use":[40],"convolutional":[42],"neural":[43],"network":[44],"(CNN)":[45],"as":[46],"surrogate":[48,73],"model.":[49],"train":[53],"model":[55,74],"on":[56,139],"pairwise":[57],"fitness":[58,98],"differences":[59],"decrease":[61],"number":[63,126,165],"evaluated":[65,167],"solutions":[66,168],"that":[67,89,129],"is":[68,92,108,130],"required":[69,131,169],"achieve":[71,133],"adequate":[72],"training.":[75],"In":[76,136],"providing":[77],"proof":[79],"principle,":[81],"consider":[83],"relatively":[84],"standard":[85],"CNNs,":[86],"and":[87,112,120,154],"demonstrate":[88],"their":[90],"capacity":[91],"already":[93],"sufficient":[94],"accurately":[96],"learn":[97],"landscapes":[99],"various":[101],"well-known":[102],"benchmark":[103],"functions.":[104],"The":[105],"proposed":[106],"CS-GOMEA":[107,147,171],"compared":[109],"GOMEA":[111],"widely-used":[114],"Bayesian-optimization-based":[115],"frameworks":[118],"SMAC":[119,153],"Hyperopt,":[121],"in":[122],"terms":[123],"evaluations":[128],"optimum.":[135],"our":[137],"experiments":[138],"binary":[140],"dimensionalities":[143],"up":[144],"to400":[145],"variables,":[146],"always":[148],"found":[149,177],"optimum,":[151],"whereas":[152],"Hyperopt":[155],"failed":[156],"problem":[158],"sizes":[159],"over":[160],"16":[161],"Moreover,":[163],"by":[170],"find":[173],"optimum":[175],"was":[176],"scale":[179],"much":[180],"better":[181],"than":[182],"GOMEA.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
