{"id":"https://openalex.org/W4312831152","doi":"https://doi.org/10.1109/tii.2022.3231419","title":"AutoML With Parallel Genetic Algorithm for Fast Hyperparameters Optimization in Efficient IoT Time Series Prediction","display_name":"AutoML With Parallel Genetic Algorithm for Fast Hyperparameters Optimization in Efficient IoT Time Series Prediction","publication_year":2022,"publication_date":"2022-12-22","ids":{"openalex":"https://openalex.org/W4312831152","doi":"https://doi.org/10.1109/tii.2022.3231419"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2022.3231419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3231419","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-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/A5061391892","display_name":"Di Wu","orcid":"https://orcid.org/0000-0001-8697-1817"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Di Wu","raw_affiliation_strings":["Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha, China","School of Design, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"School of Design, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103262923","display_name":"Qinghua Guan","orcid":"https://orcid.org/0000-0002-4930-6372"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Guan","raw_affiliation_strings":["School of Design, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Design, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085168356","display_name":"Zhe Fan","orcid":"https://orcid.org/0000-0001-6223-6228"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Fan","raw_affiliation_strings":["Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068697970","display_name":"Hanhui Deng","orcid":"https://orcid.org/0000-0003-2091-5163"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanhui Deng","raw_affiliation_strings":["School of Design, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Design, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019612664","display_name":"Tao Wu","orcid":"https://orcid.org/0000-0003-3455-7934"},"institutions":[{"id":"https://openalex.org/I173759888","display_name":"Hunan Normal University","ror":"https://ror.org/053w1zy07","country_code":"CN","type":"education","lineage":["https://openalex.org/I173759888"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Wu","raw_affiliation_strings":["Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha, China","institution_ids":["https://openalex.org/I173759888"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061391892"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":4.3035,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.94936777,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"19","issue":"9","first_page":"9555","last_page":"9564"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9847000241279602,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9782999753952026,"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/hyperparameter","display_name":"Hyperparameter","score":0.8585894107818604},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.8335613012313843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7658132314682007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6863542199134827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6854748725891113},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.6256612539291382},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5957521200180054},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5497227907180786},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5070157051086426},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4396035671234131},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4208641052246094},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4191739857196808},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.16317737102508545}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8585894107818604},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.8335613012313843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7658132314682007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6863542199134827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6854748725891113},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.6256612539291382},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5957521200180054},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5497227907180786},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5070157051086426},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4396035671234131},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4208641052246094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4191739857196808},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.16317737102508545},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2022.3231419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3231419","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1365786781","display_name":null,"funder_award_id":"61972145","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W620777765","https://openalex.org/W921744894","https://openalex.org/W1036195498","https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1974166894","https://openalex.org/W2064675550","https://openalex.org/W2097998348","https://openalex.org/W2131241448","https://openalex.org/W2134954564","https://openalex.org/W2136172106","https://openalex.org/W2250904038","https://openalex.org/W2469293760","https://openalex.org/W2552942965","https://openalex.org/W2762644836","https://openalex.org/W2771622871","https://openalex.org/W2887782657","https://openalex.org/W2954743925","https://openalex.org/W2962746461","https://openalex.org/W2964166963","https://openalex.org/W2964515685","https://openalex.org/W2975301014","https://openalex.org/W2987793235","https://openalex.org/W3004416790","https://openalex.org/W3011655242","https://openalex.org/W3034270904","https://openalex.org/W3036810572","https://openalex.org/W3042557051","https://openalex.org/W3043787446","https://openalex.org/W3044515030","https://openalex.org/W3089865507","https://openalex.org/W3094704314","https://openalex.org/W3097686945","https://openalex.org/W3099185017","https://openalex.org/W3105324058","https://openalex.org/W3110992986","https://openalex.org/W3134070916","https://openalex.org/W3157889433","https://openalex.org/W3182574379","https://openalex.org/W4289763996","https://openalex.org/W6619477082","https://openalex.org/W6624559250","https://openalex.org/W6631190155","https://openalex.org/W6674385629","https://openalex.org/W6678911119","https://openalex.org/W6680212416","https://openalex.org/W6729635839","https://openalex.org/W6751421292","https://openalex.org/W6759828284","https://openalex.org/W6766225098","https://openalex.org/W6768204257","https://openalex.org/W6773663752","https://openalex.org/W6775180354","https://openalex.org/W6780421573"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4205712847","https://openalex.org/W4281646320","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4287818966","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"With":[0],"the":[1,7,21,65,102,150,179],"development":[2],"of":[3,9,23,56,67,101,152,176],"artificial":[4],"intelligence":[5],"and":[6,95,174,188,231],"improvement":[8],"hardware":[10],"computing":[11],"power,":[12],"deep":[13,68],"learning":[14,69,109],"models":[15,187],"have":[16,129],"become":[17],"widely":[18],"used":[19,49],"in":[20,91,106,185,210],"Internet":[22],"Things":[24],"(IoT)":[25],"field,":[26],"especially":[27],"for":[28,50,64],"analyzing":[29],"spatiotemporal":[30],"data":[31],"collected":[32,195],"by":[33,196],"wireless":[34],"sensors.":[35,199],"Recurrent":[36],"neural":[37],"networks":[38],"(RNNs)":[39],"such":[40],"as":[41,61],"long":[42],"short-term":[43],"memory":[44],"(LSTM)":[45],"network":[46],"are":[47,59],"generally":[48],"these":[51,127],"time-series":[52,193],"data.":[53],"Hyperparameter":[54],"settings":[55,94],"model":[57,97],"training":[58],"regarded":[60],"essential":[62],"factors":[63],"performance":[66,226],"models.":[70],"Manually":[71],"optimizing":[72],"hyperparameters":[73,86],"not":[74],"only":[75],"cost":[76],"more":[77,82],"resources":[78],"but":[79],"also":[80],"be":[81],"likely":[83],"to":[84,149],"set":[85],"that":[87,216],"follow":[88],"stereotypes,":[89],"resulting":[90],"unreasonable":[92],"hyperparameter":[93,112,119],"poor":[96],"performance.":[98],"As":[99],"one":[100],"most":[103],"important":[104],"fields":[105],"automated":[107,111,137],"machine":[108],"research,":[110],"optimization":[113],"(HPO)":[114],"mainly":[115],"includes":[116],"grid":[117],"search,":[118],"search":[120],"based":[121,140,220],"on":[122,141,190,221,227],"genetic":[123,143],"algorithm,":[124],"etc.":[125],"Whereas":[126],"methods":[128,209],"their":[130],"own":[131],"drawbacks.":[132],"In":[133],"this":[134,154],"article,":[135],"an":[136],"HPO":[138,157,181,208,218],"method":[139,182,204,219],"parallel":[142],"algorithm":[144],"(PGA)":[145],"is":[146,183,214],"proposed.":[147],"According":[148],"process":[151],"PGA,":[153],"article":[155],"divided":[156],"into":[158],"several":[159],"stages,":[160],"including":[161],"population":[162],"initialization,":[163],"fitness":[164],"function,":[165],"tournament":[166],"selection,":[167],"crossover":[168],"operators,":[169,171],"mutation":[170],"subgroup":[172],"exchange,":[173],"end":[175],"evolution.":[177],"Then,":[178],"proposed":[180,203],"implemented":[184],"LSTM":[186],"tested":[189],"two":[191],"different":[192,211],"datasets":[194],"real-world":[197],"IoT":[198],"By":[200],"comparing":[201],"our":[202,217],"with":[205],"other":[206],"mainstream":[207],"datasets,":[212],"it":[213],"proved":[215],"PGA":[222],"shows":[223],"a":[224],"better":[225],"both":[228],"time":[229],"costs":[230],"prediction":[232],"results.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":6}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}