{"id":"https://openalex.org/W2949873992","doi":"https://doi.org/10.1145/3321707.3321730","title":"Efficient real-time hypervolume estimation with monotonically reducing error","display_name":"Efficient real-time hypervolume estimation with monotonically reducing error","publication_year":2019,"publication_date":"2019-07-03","ids":{"openalex":"https://openalex.org/W2949873992","doi":"https://doi.org/10.1145/3321707.3321730","mag":"2949873992"},"language":"en","primary_location":{"id":"doi:10.1145/3321707.3321730","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3321707.3321730","pdf_url":null,"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":"green","oa_url":"https://ore.exeter.ac.uk/repository/bitstream/10871/36825/1/fieldsend_gecco_2019_hypervolume.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065868865","display_name":"Jonathan E. Fieldsend","orcid":"https://orcid.org/0000-0002-0683-2583"},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Jonathan E. Fieldsend","raw_affiliation_strings":["University of Exeter, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter, UK","institution_ids":["https://openalex.org/I23923803"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5065868865"],"corresponding_institution_ids":["https://openalex.org/I23923803"],"apc_list":null,"apc_paid":null,"fwci":1.5696,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80921872,"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":"532","last_page":"540"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":1.0,"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":1.0,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9980999827384949,"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/monte-carlo-method","display_name":"Monte Carlo method","score":0.7071211338043213},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.628825306892395},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5874242782592773},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5700702667236328},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5588635802268982},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.521282434463501},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.5182428359985352},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.483748197555542},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4700421690940857},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4610952138900757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4427390694618225},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4409737288951874},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4328484535217285},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.4163685441017151},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.41375356912612915},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19005459547042847}],"concepts":[{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7071211338043213},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.628825306892395},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5874242782592773},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5700702667236328},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5588635802268982},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.521282434463501},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.5182428359985352},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.483748197555542},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4700421690940857},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4610952138900757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4427390694618225},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4409737288951874},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4328484535217285},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.4163685441017151},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.41375356912612915},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19005459547042847},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3321707.3321730","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3321707.3321730","pdf_url":null,"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:ore.exeter.ac.uk:10871/36825","is_oa":true,"landing_page_url":"http://hdl.handle.net/10871/36825","pdf_url":"https://ore.exeter.ac.uk/repository/bitstream/10871/36825/1/fieldsend_gecco_2019_hypervolume.pdf","source":{"id":"https://openalex.org/S4306401998","display_name":"Open Research Exeter (University of Exeter)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I23923803","host_organization_name":"University of Exeter","host_organization_lineage":["https://openalex.org/I23923803"],"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":"Conference paper"},{"id":"pmh:oai:figshare.com:article/29753633","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Efficient_Real-Time_Hypervolume_Estimation_with_Monotonically_Reducing_Error/29753633","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"Conference contribution"}],"best_oa_location":{"id":"pmh:oai:ore.exeter.ac.uk:10871/36825","is_oa":true,"landing_page_url":"http://hdl.handle.net/10871/36825","pdf_url":"https://ore.exeter.ac.uk/repository/bitstream/10871/36825/1/fieldsend_gecco_2019_hypervolume.pdf","source":{"id":"https://openalex.org/S4306401998","display_name":"Open Research Exeter (University of Exeter)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I23923803","host_organization_name":"University of Exeter","host_organization_lineage":["https://openalex.org/I23923803"],"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":"Conference paper"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2845541308","display_name":"Rapid Calibration for Operational and Strategic Digital Twins","funder_award_id":"104400","funder_id":"https://openalex.org/F4320335087","funder_display_name":"Innovate UK"},{"id":"https://openalex.org/G2865109628","display_name":null,"funder_award_id":"EP/N017846/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3157446626","display_name":"The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology","funder_award_id":"EP/N017846/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320335087","display_name":"Innovate UK","ror":"https://ror.org/05ar5fy68"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2949873992.pdf","grobid_xml":"https://content.openalex.org/works/W2949873992.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1564638059","https://openalex.org/W1957430778","https://openalex.org/W1980272771","https://openalex.org/W2009691097","https://openalex.org/W2012536979","https://openalex.org/W2038420231","https://openalex.org/W2050914643","https://openalex.org/W2100228661","https://openalex.org/W2100830227","https://openalex.org/W2102365077","https://openalex.org/W2108968575","https://openalex.org/W2109778277","https://openalex.org/W2110875796","https://openalex.org/W2125502051","https://openalex.org/W2127474646","https://openalex.org/W2518870586","https://openalex.org/W2551043114","https://openalex.org/W2620461643","https://openalex.org/W2739004567","https://openalex.org/W2767528495","https://openalex.org/W2911842990","https://openalex.org/W2962987192","https://openalex.org/W2963054067","https://openalex.org/W3037787799","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2402393579","https://openalex.org/W2090178682","https://openalex.org/W4241467429","https://openalex.org/W2073147994","https://openalex.org/W2001591765","https://openalex.org/W2384474142","https://openalex.org/W3083133203","https://openalex.org/W1588199609","https://openalex.org/W2744462909","https://openalex.org/W1971520370"],"abstract_inverted_index":{"The":[0],"hypervolume":[1,37,106],"(or":[2],"S-metric)":[3],"is":[4,21,61,75],"a":[5,26,102,154,164],"widely":[6],"used":[7,63],"quality":[8],"measure":[9],"employed":[10],"in":[11,28,42,116],"the":[12,29,46,52,86,91,95,124,138,145,191],"assessment":[13],"of":[14,32,48,94,97,104,126,137,157,167,193],"multi-":[15],"and":[16,162],"many-objective":[17],"evolutionary":[18],"algorithms.":[19],"It":[20],"also":[22],"directly":[23],"integrated":[24],"as":[25,45],"component":[27],"selection":[30],"mechanism":[31],"some":[33],"popular":[34],"optimisers.":[35],"Exact":[36],"calculation":[38],"becomes":[39],"prohibitively":[40],"expensive":[41],"real-time":[43,105,117],"applications":[44],"number":[47,96,103,125],"objectives":[49],"increases":[50],"and/or":[51],"approximation":[53],"set":[54],"grows.":[55],"Assuch,":[56],"Monte":[57,83],"Carlo":[58,84],"(MC)":[59],"sampling":[60],"often":[62],"to":[64,78],"estimate":[65],"its":[66],"value":[67],"rather":[68],"than":[69],"exactly":[70],"calculating":[71],"it.":[72],"This":[73],"estimation":[74,107,146],"inevitably":[76],"subject":[77],"error.":[79],"As":[80],"standard":[81,87],"with":[82,90,186],"approaches,":[85],"error":[88,147],"decreases":[89,149],"square":[92],"root":[93],"MC":[98,176],"samples.":[99,177],"We":[100],"propose":[101],"methods":[108,144],"for":[109,114,152,174],"unconstrained":[110],"archives":[111],"---":[112],"principally":[113],"use":[115],"convergence":[118],"analysis.":[119],"Furthermore,":[120],"we":[121],"show":[122],"how":[123],"domination":[127],"comparisons":[128],"can":[129],"be":[130],"considerably":[131],"reduced":[132],"by":[133],"exploiting":[134],"incremental":[135],"properties":[136],"approximated":[139],"Pareto":[140],"front.":[141],"In":[142],"these":[143],"monotonically":[148],"over":[150],"time":[151,170],"(i)":[153],"capped":[155],"budget":[156,166],"samples":[158],"per":[159,171],"algorithm":[160],"generation":[161,173],"(ii)":[163],"fixed":[165],"dedicated":[168],"computation":[169],"optimiser":[172],"new":[175],"Results":[178],"are":[179],"provided":[180],"using":[181],"an":[182],"illustrative":[183],"worst-case":[184],"scenario":[185],"rapid":[187],"archive":[188],"growth,":[189],"demonstrating":[190],"orders-of-magnitude":[192],"speed-up":[194],"possible.":[195]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-10-10T00:00:00"}