{"id":"https://openalex.org/W2558217526","doi":"https://doi.org/10.1109/cec.2016.7744061","title":"Adapting learning classifier systems to symbolic regression","display_name":"Adapting learning classifier systems to symbolic regression","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2558217526","doi":"https://doi.org/10.1109/cec.2016.7744061","mag":"2558217526"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2016.7744061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744061","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":"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/A5014712593","display_name":"Syed S. Naqvi","orcid":"https://orcid.org/0000-0002-6335-3538"},"institutions":[{"id":"https://openalex.org/I41156924","display_name":"Victoria University of Wellington","ror":"https://ror.org/0040r6f76","country_code":"NZ","type":"education","lineage":["https://openalex.org/I41156924"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Syed S. Naqvi","raw_affiliation_strings":["Victoria University of Wellington, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Victoria University of Wellington, New Zealand","institution_ids":["https://openalex.org/I41156924"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065533636","display_name":"Will N. Browne","orcid":"https://orcid.org/0000-0001-8979-2224"},"institutions":[{"id":"https://openalex.org/I41156924","display_name":"Victoria University of Wellington","ror":"https://ror.org/0040r6f76","country_code":"NZ","type":"education","lineage":["https://openalex.org/I41156924"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Will N. Browne","raw_affiliation_strings":["Victoria University of Wellington, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Victoria University of Wellington, New Zealand","institution_ids":["https://openalex.org/I41156924"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41156924"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.12462478,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2209","last_page":"2216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9886999726295471,"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/T11663","display_name":"Viral Infectious Diseases and Gene Expression in Insects","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/symbolic-regression","display_name":"Symbolic regression","score":0.9734033346176147},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.8579210638999939},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6396980285644531},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6268342733383179},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6216476559638977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6166226863861084},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5813487768173218},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5735209584236145},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4886622726917267},{"id":"https://openalex.org/keywords/learning-classifier-system","display_name":"Learning classifier system","score":0.4418802261352539},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2235552966594696},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20172765851020813},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1846289336681366}],"concepts":[{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.9734033346176147},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.8579210638999939},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6396980285644531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6268342733383179},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6216476559638977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166226863861084},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5813487768173218},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5735209584236145},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4886622726917267},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.4418802261352539},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2235552966594696},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20172765851020813},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1846289336681366},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec.2016.7744061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744061","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W110238773","https://openalex.org/W178471156","https://openalex.org/W1485039492","https://openalex.org/W1501730763","https://openalex.org/W1503370304","https://openalex.org/W1515057696","https://openalex.org/W1519072852","https://openalex.org/W1576818901","https://openalex.org/W1646752325","https://openalex.org/W1837450639","https://openalex.org/W1900473378","https://openalex.org/W1974805999","https://openalex.org/W1993254737","https://openalex.org/W2019885537","https://openalex.org/W2021837985","https://openalex.org/W2023131636","https://openalex.org/W2052153221","https://openalex.org/W2059396387","https://openalex.org/W2061011797","https://openalex.org/W2098888151","https://openalex.org/W2100604298","https://openalex.org/W2103298757","https://openalex.org/W2111362647","https://openalex.org/W2132196970","https://openalex.org/W2137261852","https://openalex.org/W2143810412","https://openalex.org/W2144172186","https://openalex.org/W2146721070","https://openalex.org/W2149249560","https://openalex.org/W2154369084","https://openalex.org/W2477329842","https://openalex.org/W6604500356","https://openalex.org/W6630914662","https://openalex.org/W6656397462"],"related_works":["https://openalex.org/W2968285896","https://openalex.org/W2381504162","https://openalex.org/W1567571923","https://openalex.org/W2472646430","https://openalex.org/W2610450612","https://openalex.org/W2549153708","https://openalex.org/W2897801744","https://openalex.org/W2147355282","https://openalex.org/W2394133867","https://openalex.org/W2558217526"],"abstract_inverted_index":{"Genetic":[0],"programming":[1],"(GP)":[2],"approaches":[3],"have":[4,13],"been":[5],"widely":[6],"studied":[7],"for":[8,57],"symbolic":[9,36,59,73,80],"regression":[10,37,60,81],"problems":[11],"and":[12,25,48],"achieved":[14],"substantial":[15],"progress.":[16],"This":[17,62],"work":[18],"investigates":[19],"the":[20,64,70,85,98,117],"effectiveness":[21],"of":[22,29,72,97,102,111],"niching":[23],"property":[24],"multiple":[26],"learned":[27],"solutions":[28,113],"a":[30,95],"Learning":[31],"Classifier":[32],"System":[33],"(LCS)":[34],"to":[35,68],"benchmark":[38],"problems.":[39],"Specifically,":[40],"an":[41,108],"XCS":[42],"with":[43,107,116],"real-valued":[44],"interval":[45],"based":[46],"conditions":[47],"code":[49],"fragmented":[50],"action":[51],"termed":[52],"as":[53,114],"XCS-SR":[54,87],"is":[55,63],"proposed":[56,86],"tackling":[58],"problem.":[61],"first":[65],"LCS":[66],"ever":[67],"address":[69],"problem":[71],"regression.":[74],"The":[75],"results":[76,93],"on":[77,94],"nine":[78],"standard":[79],"benchmarks":[82],"show":[83],"that":[84],"method":[88],"consistently":[89],"obtains":[90],"statistically":[91],"better":[92],"majority":[96],"benchmarks,":[99],"in":[100],"terms":[101],"average":[103],"absolute":[104],"error":[105],"together":[106],"increased":[109],"number":[110],"exact":[112],"compared":[115],"GP":[118],"benchmark.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}