{"id":"https://openalex.org/W3192124540","doi":"https://doi.org/10.1109/cec45853.2021.9504984","title":"Comparative Performances of Neural Networks of Variant Architectures Trained with Backpropagation and Differential Evolution","display_name":"Comparative Performances of Neural Networks of Variant Architectures Trained with Backpropagation and Differential Evolution","publication_year":2021,"publication_date":"2021-06-28","ids":{"openalex":"https://openalex.org/W3192124540","doi":"https://doi.org/10.1109/cec45853.2021.9504984","mag":"3192124540"},"language":"en","primary_location":{"id":"doi:10.1109/cec45853.2021.9504984","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec45853.2021.9504984","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5076201331","display_name":"Zakaria Oussalem","orcid":null},"institutions":[{"id":"https://openalex.org/I7454413","display_name":"\u00c9cole Centrale de Lille","ror":"https://ror.org/01x441g73","country_code":"FR","type":"education","lineage":["https://openalex.org/I7454413"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Zakaria Oussalem","raw_affiliation_strings":["Ecole Centrale de Lille,Department of Computer Science,Villeneuve d’Ascq,France"],"affiliations":[{"raw_affiliation_string":"Ecole Centrale de Lille,Department of Computer Science,Villeneuve d’Ascq,France","institution_ids":["https://openalex.org/I7454413"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044744826","display_name":"Rochan Avlur Venkat","orcid":null},"institutions":[{"id":"https://openalex.org/I1336379959","display_name":"Mahindra Group (India)","ror":"https://ror.org/05mxsz225","country_code":"IN","type":"company","lineage":["https://openalex.org/I1336379959"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rochan Avlur Venkat","raw_affiliation_strings":["Mahindra University,Department of Computer Science,Hyderabad,India"],"affiliations":[{"raw_affiliation_string":"Mahindra University,Department of Computer Science,Hyderabad,India","institution_ids":["https://openalex.org/I1336379959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050568213","display_name":"Jahnavi Malagavalli","orcid":null},"institutions":[{"id":"https://openalex.org/I1336379959","display_name":"Mahindra Group (India)","ror":"https://ror.org/05mxsz225","country_code":"IN","type":"company","lineage":["https://openalex.org/I1336379959"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jahnavi Malagavalli","raw_affiliation_strings":["Mahindra University,Department of Computer Science,Hyderabad,India"],"affiliations":[{"raw_affiliation_string":"Mahindra University,Department of Computer Science,Hyderabad,India","institution_ids":["https://openalex.org/I1336379959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080157265","display_name":"Arya K. Bhattacharya","orcid":"https://orcid.org/0000-0001-5144-4582"},"institutions":[{"id":"https://openalex.org/I1336379959","display_name":"Mahindra Group (India)","ror":"https://ror.org/05mxsz225","country_code":"IN","type":"company","lineage":["https://openalex.org/I1336379959"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arya Kumar Bhattacharya","raw_affiliation_strings":["Mahindra University,Department of Computer Science,Hyderabad,India"],"affiliations":[{"raw_affiliation_string":"Mahindra University,Department of Computer Science,Hyderabad,India","institution_ids":["https://openalex.org/I1336379959"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076201331"],"corresponding_institution_ids":["https://openalex.org/I7454413"],"apc_list":null,"apc_paid":null,"fwci":0.4079,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68527368,"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":"1209","last_page":"1216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9997000098228455,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9997000098228455,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9994000196456909,"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":0.9937000274658203,"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/backpropagation","display_name":"Backpropagation","score":0.8789870142936707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.714820146560669},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6915655136108398},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6814401149749756},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.603410005569458},{"id":"https://openalex.org/keywords/differential-evolution","display_name":"Differential evolution","score":0.5634978413581848},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.547838032245636},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5112340450286865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.490190327167511},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4898044764995575},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48561111092567444},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43873822689056396},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3158901333808899},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19457679986953735}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.8789870142936707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.714820146560669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6915655136108398},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6814401149749756},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.603410005569458},{"id":"https://openalex.org/C74750220","wikidata":"https://www.wikidata.org/wiki/Q2662197","display_name":"Differential evolution","level":2,"score":0.5634978413581848},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.547838032245636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5112340450286865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.490190327167511},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4898044764995575},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48561111092567444},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43873822689056396},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3158901333808899},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19457679986953735},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec45853.2021.9504984","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec45853.2021.9504984","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1581478146","https://openalex.org/W1595159159","https://openalex.org/W1659842140","https://openalex.org/W1708394971","https://openalex.org/W1825675169","https://openalex.org/W1849277567","https://openalex.org/W2011980475","https://openalex.org/W2064675550","https://openalex.org/W2116267050","https://openalex.org/W2152195021","https://openalex.org/W2156194072","https://openalex.org/W2157331557","https://openalex.org/W2240067561","https://openalex.org/W2253993278","https://openalex.org/W2472996304","https://openalex.org/W2543580944","https://openalex.org/W2767002384","https://openalex.org/W2778749116","https://openalex.org/W2875435464","https://openalex.org/W2888728044","https://openalex.org/W2962851944","https://openalex.org/W2963588848","https://openalex.org/W2963778169","https://openalex.org/W2964148549","https://openalex.org/W2964331719","https://openalex.org/W2997917529","https://openalex.org/W3031790649","https://openalex.org/W3035005828","https://openalex.org/W3161570989","https://openalex.org/W3188325035","https://openalex.org/W4298135002","https://openalex.org/W4300485620","https://openalex.org/W4300687870","https://openalex.org/W6638389677","https://openalex.org/W6639204139","https://openalex.org/W6677359150","https://openalex.org/W6685133223","https://openalex.org/W6691820373","https://openalex.org/W6746622358","https://openalex.org/W6753303928","https://openalex.org/W6778747846"],"related_works":["https://openalex.org/W2997541400","https://openalex.org/W4312192474","https://openalex.org/W4311257506","https://openalex.org/W2480330148","https://openalex.org/W2772371429","https://openalex.org/W2774265021","https://openalex.org/W4285183766","https://openalex.org/W4221136938","https://openalex.org/W4310989423","https://openalex.org/W3156786002"],"abstract_inverted_index":{"Work":[0],"on":[1,9,175,178,190],"Neuro-evolution":[2],"has":[3],"of":[4,12,20,49,56,61,86,100,114,128,206],"late":[5],"tended":[6],"to":[7,23,96,183],"focus":[8],"the":[10,34,84,141,198,202],"design":[11],"alternative":[13,119],"architectures":[14],"and":[15,26,117,120,137,146,151,187,208,212],"hyper-parameters":[16],"for":[17,37,73,105,148,200],"different":[18],"classes":[19],"Neural":[21],"Networks":[22],"improve":[24],"performance":[25,133],"computational":[27],"efficiency,":[28],"using":[29],"Evolutionary":[30],"Algorithms":[31],"(EA).":[32],"Predominantly,":[33],"underlying":[35],"mechanism":[36],"learning":[38],"weight":[39],"parameters":[40],"remains":[41],"traditional":[42],"backpropagation":[43],"(BP),":[44],"now":[45],"considered":[46],"a":[47,125],"paradigm":[48],"Deep":[50],"Learning":[51],"(DL).":[52],"Many":[53],"important":[54],"facets":[55,113,205],"DL,":[57,207],"like":[58],"hierarchical":[59],"construction":[60],"features":[62],"across":[63],"layers":[64],"in":[65,94],"image":[66,192],"recognition,":[67],"vanishing":[68],"gradients,":[69],"etc.,":[70],"are":[71],"taken":[72],"granted":[74],"without":[75],"recognizing":[76],"that":[77,90,163],"these":[78,111],"may":[79],"implicitly":[80],"be":[81],"induced":[82],"by":[83],"properties":[85],"BP":[87,185],"itself.":[88],"EAs":[89],"perform":[91],"global":[92],"optimization":[93],"contrast":[95],"local":[97,168],"gradient":[98],"descent":[99],"BP,":[101,138],"if":[102],"used":[103],"extensively":[104],"ANN":[106],"training,":[107],"can":[108,170],"potentially":[109,209],"disrupt":[110],"assumed":[112,204],"DL":[115],"-":[116],"construct":[118],"interesting":[121,213],"perspectives.":[122,214],"There":[123],"is":[124,161],"surprising":[126],"lack":[127],"research":[129],"activity":[130],"towards":[131],"one-to-one":[132],"comparison":[134],"between":[135],"EA":[136],"keeping":[139],"precisely":[140],"same":[142],"architectures,":[143],"activation":[144],"functions":[145],"datasets,":[147],"complex":[149],"regression":[150,176],"classification":[152,193],"problems.":[153],"This":[154],"work":[155],"partially":[156],"fills":[157],"this":[158,196],"gap.":[159],"It":[160],"demonstrated":[162],"Differential":[164],"Evolution":[165],"enhanced":[166],"with":[167],"search,":[169],"generate":[171],"mildly":[172],"better":[173],"accuracies":[174,189],"problems":[177],"noisy":[179],"industrial":[180],"datasets":[181],"compared":[182],"standard":[184],"solutions,":[186],"comparable":[188],"popular":[191],"datasets.":[194],"Consequently,":[195],"establishes":[197],"baseline":[199],"revisiting":[201],"above-mentioned":[203],"engender":[210],"new":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-12-04T23:47:47.292601","created_date":"2025-10-10T00:00:00"}