{"id":"https://openalex.org/W2558781553","doi":"https://doi.org/10.1109/cec.2016.7744117","title":"An effective estimation of distribution algorithm for solving uniform parallel machine scheduling problem with precedence constraints","display_name":"An effective estimation of distribution algorithm for solving uniform parallel machine scheduling problem with precedence constraints","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2558781553","doi":"https://doi.org/10.1109/cec.2016.7744117","mag":"2558781553"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2016.7744117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Congress on Evolutionary Computation (CEC)","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/A5022387737","display_name":"Chu\u2010ge Wu","orcid":"https://orcid.org/0000-0003-2492-4817"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chu-ge Wu","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398661","display_name":"Ling Wang","orcid":"https://orcid.org/0000-0001-8964-6454"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Wang","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060249411","display_name":"Xiaolong Zheng","orcid":"https://orcid.org/0000-0001-7950-6773"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-long Zheng","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5022387737"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.5071,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.90427121,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"2626","last_page":"2632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12288","display_name":"Optimization and Search Problems","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9926999807357788,"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/estimation-of-distribution-algorithm","display_name":"Estimation of distribution algorithm","score":0.7691299915313721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7412703633308411},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.6110719442367554},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5395772457122803},{"id":"https://openalex.org/keywords/permutation","display_name":"Permutation (music)","score":0.48526403307914734},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4833364188671112},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4663124978542328},{"id":"https://openalex.org/keywords/taguchi-methods","display_name":"Taguchi methods","score":0.4638493061065674},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.43611517548561096},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20304113626480103},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16212013363838196}],"concepts":[{"id":"https://openalex.org/C162500139","wikidata":"https://www.wikidata.org/wiki/Q2835887","display_name":"Estimation of distribution algorithm","level":2,"score":0.7691299915313721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7412703633308411},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6110719442367554},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5395772457122803},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.48526403307914734},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4833364188671112},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4663124978542328},{"id":"https://openalex.org/C83469408","wikidata":"https://www.wikidata.org/wiki/Q2036525","display_name":"Taguchi methods","level":2,"score":0.4638493061065674},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.43611517548561096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20304113626480103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16212013363838196},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec.2016.7744117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1479911746","https://openalex.org/W1526035966","https://openalex.org/W1540706608","https://openalex.org/W1981416984","https://openalex.org/W1989926519","https://openalex.org/W1991295710","https://openalex.org/W2009835506","https://openalex.org/W2020766324","https://openalex.org/W2035876948","https://openalex.org/W2037166173","https://openalex.org/W2046176112","https://openalex.org/W2046471719","https://openalex.org/W2056361826","https://openalex.org/W2065962833","https://openalex.org/W2071277483","https://openalex.org/W2079748944","https://openalex.org/W2095363678","https://openalex.org/W2130418601","https://openalex.org/W2155359179","https://openalex.org/W2161640904","https://openalex.org/W2408697637","https://openalex.org/W4205902353","https://openalex.org/W4250545504","https://openalex.org/W6628782388","https://openalex.org/W6631510689","https://openalex.org/W6682694301","https://openalex.org/W6980993111"],"related_works":["https://openalex.org/W2141801573","https://openalex.org/W2042858286","https://openalex.org/W2186534316","https://openalex.org/W1899929387","https://openalex.org/W2104509673","https://openalex.org/W1660662692","https://openalex.org/W778881360","https://openalex.org/W2289064120","https://openalex.org/W2047779624","https://openalex.org/W1748556730"],"abstract_inverted_index":{"In":[0,24,91],"this":[1],"paper,":[2],"an":[3,70],"effective":[4,52],"estimation":[5],"of":[6,62,96,106,112,125],"distributed":[7],"algorithm":[8,140],"(eEDA)":[9],"is":[10,31,40,55,76,82,100],"proposed":[11,83],"to":[12,42,46,57,84,102,121],"solve":[13],"the":[14,25,27,34,44,47,59,63,93,104,110,113,123,126,130,135,138],"uniform":[15],"parallel":[16],"machine":[17],"scheduling":[18],"problem":[19],"with":[20,88],"precedence":[21],"constraints":[22],"(prec-UFPMSP).":[23],"eEDA,":[26],"permutation-based":[28],"encoding":[29],"scheme":[30],"adopted":[32],"and":[33,78,129],"earliest":[35],"finish":[36],"time":[37],"(EFT)":[38],"method":[39,75,95,99],"used":[41,101],"decode":[43],"solutions":[45,87],"detail":[48],"schedules.":[49],"A":[50],"new":[51],"probability":[53,127],"model":[54],"designed":[56],"describe":[58],"relative":[60],"positions":[61],"jobs.":[64],"Based":[65],"on":[66,109],"such":[67],"a":[68,79],"model,":[69,128],"incremental":[71],"learning":[72],"based":[73],"updating":[74],"developed":[77],"sampling":[80],"mechanism":[81],"generate":[85],"feasible":[86],"good":[89],"diversity.":[90],"addition,":[92],"Taguchi":[94],"design-of-experiment":[97],"(DOE)":[98],"investigate":[103],"effect":[105],"key":[107],"parameters":[108],"performance":[111],"eEDA.":[114],"Finally,":[115],"numerical":[116],"tests":[117],"are":[118],"carried":[119],"out":[120],"demonstrate":[122],"superiority":[124],"comparative":[131],"results":[132],"show":[133],"that":[134],"eEDA":[136],"outperforms":[137],"existing":[139],"for":[141],"most":[142],"cases.":[143]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}