{"id":"https://openalex.org/W2556256767","doi":"https://doi.org/10.1109/ijcnn.2016.7727320","title":"A hybrid neuro-evolutive algorithm for neural network optimization","display_name":"A hybrid neuro-evolutive algorithm for neural network optimization","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2556256767","doi":"https://doi.org/10.1109/ijcnn.2016.7727320","mag":"2556256767"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5091285556","display_name":"L\u00eddio Mauro Lima de Campos","orcid":"https://orcid.org/0000-0003-4315-829X"},"institutions":[{"id":"https://openalex.org/I59606676","display_name":"Universidade Federal do Par\u00e1","ror":"https://ror.org/03q9sr818","country_code":"BR","type":"education","lineage":["https://openalex.org/I59606676"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Lidio Mauro Lima de Campos","raw_affiliation_strings":["Prog.de Pos-Graduacao em, Engenharia El\u00e9trica, Universidade Federal do Par\u00e1-UFPA, Brasil"],"affiliations":[{"raw_affiliation_string":"Prog.de Pos-Graduacao em, Engenharia El\u00e9trica, Universidade Federal do Par\u00e1-UFPA, Brasil","institution_ids":["https://openalex.org/I59606676"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101868357","display_name":"Oliveira J\u00fanior","orcid":"https://orcid.org/0000-0002-6640-3182"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roberto Celio Limao de Oliveira","raw_affiliation_strings":["Faculty of Computer Engineering, Federal University of Par\u00e1, Universidade Federal do Para, Belem, PA, BR"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Engineering, Federal University of Par\u00e1, Universidade Federal do Para, Belem, PA, BR","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090517396","display_name":"Mauro Roisenberg","orcid":"https://orcid.org/0000-0001-9707-0360"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mauro Roisenberg","raw_affiliation_strings":["Departamento de Inform\u00e1tica e Estatistica, Universidade Federal de Santa Catarina-UFSC Florian6polis, SC, Brasil"],"affiliations":[{"raw_affiliation_string":"Departamento de Inform\u00e1tica e Estatistica, Universidade Federal de Santa Catarina-UFSC Florian6polis, SC, Brasil","institution_ids":["https://openalex.org/I4104125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091285556"],"corresponding_institution_ids":["https://openalex.org/I59606676"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08016635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"1096","last_page":"1103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9987000226974487,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9987000226974487,"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":0.9973999857902527,"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/T10320","display_name":"Neural Networks and Applications","score":0.9901000261306763,"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":0.738949179649353},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7122603058815002},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5785369873046875},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5159462094306946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5137108564376831},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4545610547065735},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.42942026257514954},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4232170581817627},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41606080532073975},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36176827549934387}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.738949179649353},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7122603058815002},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5785369873046875},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5159462094306946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5137108564376831},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4545610547065735},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.42942026257514954},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4232170581817627},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41606080532073975},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36176827549934387},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W157468466","https://openalex.org/W165295313","https://openalex.org/W1581066146","https://openalex.org/W1614611390","https://openalex.org/W1964646852","https://openalex.org/W1966991711","https://openalex.org/W1971676152","https://openalex.org/W2012391952","https://openalex.org/W2023406419","https://openalex.org/W2028967620","https://openalex.org/W2031518108","https://openalex.org/W2047408307","https://openalex.org/W2050870241","https://openalex.org/W2072782187","https://openalex.org/W2111935653","https://openalex.org/W2126137228","https://openalex.org/W2136848732","https://openalex.org/W2138321920","https://openalex.org/W2144644840","https://openalex.org/W2153684665","https://openalex.org/W2165085255","https://openalex.org/W2165132362","https://openalex.org/W4229539396","https://openalex.org/W6606439196","https://openalex.org/W6634833660"],"related_works":["https://openalex.org/W2019887508","https://openalex.org/W2372415543","https://openalex.org/W2354205711","https://openalex.org/W2808717917","https://openalex.org/W2377292223","https://openalex.org/W2366584243","https://openalex.org/W2376563992","https://openalex.org/W2360006733","https://openalex.org/W4304590249","https://openalex.org/W2366368367"],"abstract_inverted_index":{"This":[0,54],"paper":[1],"proposes":[2],"a":[3,10,42,73],"hybrid":[4],"neuro-evolutive":[5],"algorithm":[6,39],"(NEA)":[7],"that":[8,45,78,101],"uses":[9,72],"compact":[11],"indirect":[12],"encoding":[13],"scheme":[14],"(IES)":[15],"for":[16,118],"representing":[17],"its":[18],"genotypes,":[19],"moreover":[20],"has":[21],"the":[22,26,50,70,81,87,93,148,152,161,171,179],"ability":[23],"to":[24,48,66,170],"reuse":[25],"genotypes":[27],"and":[28,33,60,84,114,132,139,156,174],"automatically":[29],"build":[30],"modular,":[31],"hierarchical":[32],"recurrent":[34],"neural":[35,51,56,98],"networks.":[36],"A":[37],"genetic":[38],"(GA)":[40],"evolves":[41],"Lindenmayer":[43],"System":[44],"is":[46],"used":[47],"design":[49],"network's":[52],"architecture.":[53],"basic":[55],"codification":[57],"confers":[58],"scalability":[59],"search":[61],"space":[62],"reduction":[63],"in":[64,178],"relation":[65],"other":[67,149],"methods.":[68],"Furthermore,":[69],"system":[71],"parallel":[74],"genome":[75],"scan":[76],"engine":[77],"increases":[79],"both":[80],"implicit":[82],"parallelism":[83],"convergence":[85],"of":[86,92,176],"GA.":[88],"The":[89,105,123],"fitness":[90],"function":[91],"NEA":[94,106,146,177],"rewards":[95],"economical":[96],"artificial":[97],"networks":[99],"(ANNs)":[100],"are":[102,125,168],"easily":[103],"implemented.":[104],"was":[107],"tested":[108],"on":[109],"five":[110],"real-world":[111],"classification":[112,155],"datasets":[113,117],"three":[115],"well-known":[116],"time":[119,157],"series":[120,158],"forecasting":[121,134,159],"(TSF).":[122],"results":[124,167],"statistically":[126],"compared":[127],"against":[128],"established":[129],"state-of-the-art":[130],"algorithms":[131],"various":[133],"methods":[135],"(ADANN,":[136],"ARIMA,":[137],"UCM,":[138],"Forecast":[140],"Pro).":[141],"In":[142],"most":[143,153],"cases,":[144],"our":[145],"outperformed":[147],"methods,":[150],"delivering":[151],"accurate":[154],"with":[160],"least":[162],"computational":[163],"effort.":[164],"These":[165],"superior":[166],"attributed":[169],"improved":[172],"effectiveness":[173],"efficiency":[175],"decision-making":[180],"process.":[181]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
