{"id":"https://openalex.org/W2004281489","doi":"https://doi.org/10.1080/09528130110067142","title":"Some experimental evidence on the performance of GA-designed neural networks","display_name":"Some experimental evidence on the performance of GA-designed neural networks","publication_year":2001,"publication_date":"2001-07-01","ids":{"openalex":"https://openalex.org/W2004281489","doi":"https://doi.org/10.1080/09528130110067142","mag":"2004281489"},"language":"en","primary_location":{"id":"doi:10.1080/09528130110067142","is_oa":false,"landing_page_url":"https://doi.org/10.1080/09528130110067142","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental &amp; Theoretical Artificial Intelligence","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/A5101471712","display_name":"James V. Hansen","orcid":"https://orcid.org/0000-0001-9785-2776"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James V. Hansen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080595326","display_name":"James B. McDonald","orcid":"https://orcid.org/0000-0002-3919-5058"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James B. McDonald","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.10029155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"13","issue":"3","first_page":"307","last_page":"321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9987999796867371,"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/T10320","display_name":"Neural Networks and Applications","score":0.9987999796867371,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.984499990940094,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9782000184059143,"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":0.8454104661941528},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7692228555679321},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.6896000504493713},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.6891883611679077},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.6613186597824097},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6478075981140137},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6221650242805481},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.595416247844696},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5403274893760681},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3678721785545349}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8454104661941528},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7692228555679321},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.6896000504493713},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6891883611679077},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.6613186597824097},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6478075981140137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6221650242805481},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.595416247844696},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5403274893760681},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3678721785545349},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/09528130110067142","is_oa":false,"landing_page_url":"https://doi.org/10.1080/09528130110067142","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental &amp; Theoretical Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W40343832","https://openalex.org/W647344759","https://openalex.org/W1511493290","https://openalex.org/W1533618852","https://openalex.org/W1561539683","https://openalex.org/W1586335931","https://openalex.org/W1602877939","https://openalex.org/W1659842140","https://openalex.org/W1946342668","https://openalex.org/W1978589351","https://openalex.org/W1979444676","https://openalex.org/W1994530392","https://openalex.org/W2013073784","https://openalex.org/W2089763487","https://openalex.org/W2094101722","https://openalex.org/W2098545770","https://openalex.org/W2103496339","https://openalex.org/W2112865076","https://openalex.org/W2124503759","https://openalex.org/W2132166479","https://openalex.org/W2134817998","https://openalex.org/W2138784882","https://openalex.org/W2149903656","https://openalex.org/W2152559190","https://openalex.org/W4210925605","https://openalex.org/W4247346963","https://openalex.org/W4388297464"],"related_works":["https://openalex.org/W3177062893","https://openalex.org/W3125143773","https://openalex.org/W803550684","https://openalex.org/W2007032764","https://openalex.org/W2483226803","https://openalex.org/W3143937874","https://openalex.org/W2067280619","https://openalex.org/W4251343851","https://openalex.org/W4352977312","https://openalex.org/W2041180560"],"abstract_inverted_index":{"While":[0],"some":[1],"efforts":[2],"have":[3,63],"been":[4],"made":[5],"to":[6,28,31,47,65],"formalize":[7],"the":[8,19,26,49,67,111,130],"choice":[9],"of":[10,36,39,43,52,69,72],"parameters":[11,56,96],"for":[12],"artificial":[13],"neural":[14,53,93],"networks":[15,54,94],"(multi-level":[16],"perceptrons)":[17],"using":[18,99],"backpropagation":[20],"(BP)":[21],"algorithm,":[22],"studies":[23],"reported":[24],"in":[25],"literature":[27],"date":[29],"appear":[30],"be":[32],"dominated":[33],"by":[34,58,74],"use":[35],"heuristic":[37,100,127],"methods":[38,128],"design.":[40],"The":[41],"objective":[42],"this":[44,70],"paper":[45],"is":[46,90],"test":[48],"comparative":[50],"performance":[51],"with":[55],"determined":[57],"genetic":[59],"algorithms":[60],"(GAs).":[61],"We":[62],"attempted":[64],"improve":[66],"quality":[68],"type":[71],"research":[73],"comparing":[75],"several":[76,105],"models":[77,118],"across":[78],"seven":[79],"real":[80],"problem":[81,112],"domains,":[82],"including":[83],"both":[84],"regression":[85],"and":[86,129],"classification":[87],"problems.":[88],"Performance":[89],"benchmarked":[91],"against":[92],"whose":[95],"are":[97],"selected":[98],"methods,":[101],"as":[102,104,122],"well":[103,123],"widely":[106],"used":[107],"statistical":[108,131],"models.":[109,132],"For":[110],"domains":[113],"tested,":[114],"GA-assisted":[115],"design":[116],"yields":[117],"that":[119],"consistently":[120],"perform":[121],"or":[124],"better":[125],"than":[126]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
