{"id":"https://openalex.org/W4376983396","doi":"https://doi.org/10.1145/3583131.3590470","title":"Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding Exploration","display_name":"Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding Exploration","publication_year":2023,"publication_date":"2023-07-12","ids":{"openalex":"https://openalex.org/W4376983396","doi":"https://doi.org/10.1145/3583131.3590470"},"language":"en","primary_location":{"id":"doi:10.1145/3583131.3590470","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583131.3590470","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583131.3590470","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":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583131.3590470","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027494765","display_name":"Hannah Janmohamed","orcid":"https://orcid.org/0000-0001-7997-8455"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hannah Janmohamed","raw_affiliation_strings":["Imperial College, London, United Kingdom","InstaDeep, London, United Kingdom","Imperial College, London, United Kingdom InstaDeep, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-7997-8455","affiliations":[{"raw_affiliation_string":"Imperial College, London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"InstaDeep, London, United Kingdom","institution_ids":[]},{"raw_affiliation_string":"Imperial College, London, United Kingdom InstaDeep, London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015141560","display_name":"Thomas Pierrot","orcid":"https://orcid.org/0000-0002-5227-6194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Pierrot","raw_affiliation_strings":["InstaDeep, Boston, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-5227-6194","affiliations":[{"raw_affiliation_string":"InstaDeep, Boston, United States of America","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011747084","display_name":"Antoine Cully","orcid":"https://orcid.org/0000-0002-3190-7073"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Antoine Cully","raw_affiliation_strings":["Imperial College, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-3190-7073","affiliations":[{"raw_affiliation_string":"Imperial College, London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9444,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66430039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"165","last_page":"173"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9950000047683716,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9933000206947327,"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/computer-science","display_name":"Computer science","score":0.7202886939048767},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4988408088684082},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4905323386192322},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.4836234152317047},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.4581420123577118},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4512150287628174},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4414484202861786},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.42896467447280884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3020268678665161},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18481537699699402},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15554675459861755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7202886939048767},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4988408088684082},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4905323386192322},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.4836234152317047},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.4581420123577118},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4512150287628174},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4414484202861786},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.42896467447280884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3020268678665161},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18481537699699402},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15554675459861755},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3583131.3590470","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583131.3590470","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583131.3590470","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":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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:spiral.imperial.ac.uk:10044/1/104027","is_oa":true,"landing_page_url":"http://hdl.handle.net/10044/1/104027","pdf_url":null,"source":{"id":"https://openalex.org/S4306401396","display_name":"Spiral (Imperial College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I47508984","host_organization_name":"Imperial College London","host_organization_lineage":["https://openalex.org/I47508984"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"GECCO 2023","raw_type":"Conference Paper"}],"best_oa_location":{"id":"doi:10.1145/3583131.3590470","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583131.3590470","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583131.3590470","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":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4376983396.pdf","grobid_xml":"https://content.openalex.org/works/W4376983396.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1558919105","https://openalex.org/W2020320008","https://openalex.org/W2054145446","https://openalex.org/W2739858255","https://openalex.org/W2797210984","https://openalex.org/W2937060154","https://openalex.org/W2945875572","https://openalex.org/W2957084162","https://openalex.org/W2962687375","https://openalex.org/W2962878123","https://openalex.org/W2994369270","https://openalex.org/W3041043431","https://openalex.org/W3103183699","https://openalex.org/W3103202942","https://openalex.org/W3131342066","https://openalex.org/W3169972599","https://openalex.org/W3175917212","https://openalex.org/W3200285024","https://openalex.org/W4223426965","https://openalex.org/W4252684946","https://openalex.org/W4285734657","https://openalex.org/W4287266177","https://openalex.org/W6796293393"],"related_works":["https://openalex.org/W2090178682","https://openalex.org/W4318426920","https://openalex.org/W2001591765","https://openalex.org/W2080708212","https://openalex.org/W2055791821","https://openalex.org/W4241467429","https://openalex.org/W2906115061","https://openalex.org/W2977596624","https://openalex.org/W2073147994","https://openalex.org/W2043126582"],"abstract_inverted_index":{"Quality-Diversity":[0],"(QD)":[1],"algorithms":[2],"have":[3],"recently":[4],"gained":[5],"traction":[6],"as":[7],"optimisation":[8,120],"methods":[9],"due":[10],"to":[11,34,110,121,133,140,163],"their":[12],"effectiveness":[13],"at":[14],"escaping":[15],"local":[16],"optima":[17],"and":[18,23,59,99,115,139,156,162,178,185,192],"capability":[19],"of":[20,46,72,189],"generating":[21],"wide-ranging":[22],"high-performing":[24],"solutions.":[25,73],"Recently,":[26],"Multi-Objective":[27,94],"MAP-Elites":[28,95],"(MOME)":[29],"extended":[30],"the":[31,35,187],"QD":[32,105],"paradigm":[33],"multi-objective":[36,63],"setting":[37],"by":[38,78],"maintaining":[39],"a":[40,47,52,69,103,164],"Pareto":[41,145],"front":[42],"in":[43,85,150,194],"each":[44],"cell":[45],"MAP-ELITES":[48],"grid.":[49],"mome":[50],"achieved":[51],"global":[53],"performance":[54,166,188],"that":[55,107,158,173],"competed":[56],"with":[57,96],"NSGA-II":[58,191],"SPEA2,":[60],"two":[61],"well-established":[62],"evolutionary":[64],"algorithms,":[65],"while":[66],"also":[67,129],"acquiring":[68],"diverse":[70],"repertoire":[71],"However,":[74],"MOME":[75,109,184],"is":[76,175],"limited":[77],"non-directed":[79],"genetic":[80],"search":[81,87],"mechanisms":[82,132],"which":[83],"struggle":[84],"high-dimensional":[86],"spaces.":[88],"In":[89],"this":[90],"work,":[91],"we":[92],"present":[93],"Policy-Gradient":[97],"Assistance":[98],"Crowding-based":[100],"Exploration":[101],"(MOME-PGX):":[102],"new":[104],"algorithm":[106],"extends":[108],"improve":[111],"its":[112],"data":[113],"efficiency":[114],"performance.":[116,127],"MOME-PGX":[117,149,174],"uses":[118],"gradient-based":[119],"efficiently":[122],"drive":[123],"solutions":[124],"towards":[125],"higher":[126,165],"It":[128],"introduces":[130],"crowding-based":[131],"create":[134],"an":[135],"improved":[136],"exploration":[137],"strategy":[138],"encourage":[141],"greater":[142],"uniformity":[143],"across":[144],"fronts.":[146],"We":[147,171],"evaluate":[148],"four":[151],"simulated":[152],"robot":[153],"locomotion":[154],"tasks":[155],"demonstrate":[157],"it":[159],"converges":[160],"faster":[161],"than":[167,183],"all":[168],"other":[169],"baselines.":[170],"show":[172],"between":[176],"4.3":[177],"42":[179],"times":[180],"more":[181],"data-efficient":[182],"doubles":[186],"MOME,":[190],"SPEA2":[193],"challenging":[195],"environments.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}