{"id":"https://openalex.org/W3083488387","doi":"https://doi.org/10.1109/cec48606.2020.9185680","title":"Evolution of Convolution Neural Network Architectures using Genetic Algorithm","display_name":"Evolution of Convolution Neural Network Architectures using Genetic Algorithm","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3083488387","doi":"https://doi.org/10.1109/cec48606.2020.9185680","mag":"3083488387"},"language":"en","primary_location":{"id":"doi:10.1109/cec48606.2020.9185680","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185680","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5090817532","display_name":"Aadi Swadipto Mondal","orcid":null},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aadi Swadipto Mondal","raw_affiliation_strings":["Electronics and Electncal Communication Engineering, Indian Institute of Technology (IIT) Kharagpur, West Bengal, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronics and Electncal Communication Engineering, Indian Institute of Technology (IIT) Kharagpur, West Bengal, India","institution_ids":["https://openalex.org/I145894827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5090817532"],"corresponding_institution_ids":["https://openalex.org/I145894827"],"apc_list":null,"apc_paid":null,"fwci":0.1709,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.46780763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9969000220298767,"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/T12676","display_name":"Machine Learning and ELM","score":0.9955999851226807,"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/mnist-database","display_name":"MNIST database","score":0.9094823598861694},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8061014413833618},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.692133367061615},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6413161754608154},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.60042405128479},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5723645091056824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49306175112724304},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4534868597984314},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4515506327152252},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.44882848858833313},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43625307083129883},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4255455732345581},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4252401888370514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37662047147750854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21930387616157532}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9094823598861694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061014413833618},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.692133367061615},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6413161754608154},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.60042405128479},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5723645091056824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49306175112724304},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4534868597984314},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4515506327152252},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.44882848858833313},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43625307083129883},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4255455732345581},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4252401888370514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37662047147750854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21930387616157532},{"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/cec48606.2020.9185680","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185680","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1686810756","https://openalex.org/W1921523184","https://openalex.org/W1964153772","https://openalex.org/W2064675550","https://openalex.org/W2112796928","https://openalex.org/W2126105956","https://openalex.org/W2163605009","https://openalex.org/W2302255633","https://openalex.org/W2782417188","https://openalex.org/W2892166671","https://openalex.org/W2899675781","https://openalex.org/W2946000870","https://openalex.org/W2950621961","https://openalex.org/W2969379352","https://openalex.org/W4255158661","https://openalex.org/W4288280365","https://openalex.org/W6626481562","https://openalex.org/W6637373629","https://openalex.org/W6640185926","https://openalex.org/W6698183232","https://openalex.org/W6756026151"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W4388700941"],"abstract_inverted_index":{"Convolution":[0],"Neural":[1],"Network":[2],"(CNN)":[3],"has":[4],"become":[5],"a":[6,32,52,101],"remarkable":[7],"tool":[8],"in":[9,17,164,215],"solving":[10],"many":[11],"real-world":[12],"problems":[13],"of":[14,23,25,73,115,144,162,192,209,211,245],"computer":[15],"vision":[16],"recent":[18],"years.":[19],"However,":[20],"the":[21,71,87,116,125,136,142,148,153,156,160,165,173,190,193,198,201,207,212,218,226,241],"designing":[22],"most":[24],"CNN":[26,54,79,83,127,149,194,249],"architectures":[27,80],"is":[28,90,223,243],"manual,":[29],"which":[30,251],"requires":[31],"significant":[33],"trial":[34],"and":[35,61,98,159,183,206,233],"error":[36],"methodology":[37],"as":[38],"CNN's":[39],"performance":[40],"highly":[41],"relies":[42],"on":[43,100,124,225],"their":[44],"architectures.":[45],"Thus,":[46],"it":[47],"gets":[48],"arduous":[49],"to":[50,69,77,217],"design":[51,78],"promising":[53],"architecture":[55,84],"without":[56],"having":[57],"sufficient":[58],"domain":[59],"knowledge":[60],"human":[62],"expertise.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67],"attempt":[68],"explore":[70],"possibility":[72],"using":[74,94,176],"Genetic":[75,88],"Algorithm":[76,89,97],"automatically.":[81],"The":[82,108],"proposed":[85,221],"by":[86,196],"trained":[91],"from":[92],"scratch":[93],"Gradient-":[95],"Descent":[96],"evaluated":[99],"validation":[102],"set":[103],"at":[104],"each":[105],"evolutionary":[106],"step.":[107],"algorithm":[109,222,242],"does":[110],"not":[111],"require":[112],"any":[113,122],"preprocessing":[114],"data":[117],"before":[118],"its":[119],"execution,":[120],"nor":[121],"post-processing":[123],"evolved":[126,171],"architecture.":[128,150],"We":[129,185],"propose":[130],"an":[131],"encoding":[132,154,202],"scheme":[133,140],"for":[134,189,229],"determining":[135],"layer":[137,167,214],"connectivity.":[138],"This":[139],"allows":[141],"formation":[143,191],"skip":[145],"connections":[146],"within":[147],"Along":[151],"with":[152],"scheme,":[155,203],"filter":[157,204],"dimensions,":[158,205],"number":[161,208],"nodes":[163,210],"fully-connected":[166,213],"are":[168,252],"also":[169],"genetically":[170],"during":[172],"subsequent":[174],"evolution":[175],"standard":[177],"genetic":[178,219],"operators,":[179],"namely":[180],"selection,":[181],"mutation,":[182],"crossover.":[184],"have":[186,238],"specified":[187],"algorithms":[188],"model":[195],"combining":[197],"information":[199],"about":[200],"addition":[216],"operations.The":[220],"tested":[224],"MNIST":[227],"dataset":[228],"handwritten":[230],"digit":[231],"recognition":[232],"Fashion-MNIST":[234],"dataset.":[235],"Our":[236],"experiments":[237],"shown":[239],"that":[240],"capable":[244],"successfully":[246],"generating":[247],"high-quality":[248],"architectures,":[250],"less":[253],"studied":[254],"before.":[255]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}