{"id":"https://openalex.org/W3017946098","doi":"https://doi.org/10.1145/3377930.3390152","title":"Symbolic regression driven by training data and prior knowledge","display_name":"Symbolic regression driven by training data and prior knowledge","publication_year":2020,"publication_date":"2020-06-25","ids":{"openalex":"https://openalex.org/W3017946098","doi":"https://doi.org/10.1145/3377930.3390152","mag":"3017946098"},"language":"en","primary_location":{"id":"doi:10.1145/3377930.3390152","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3377930.3390152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.11947","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ji\u0159\u00ed Kubal\u00edk","orcid":null},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Ji\u0159\u00ed Kubal\u00edk","raw_affiliation_strings":["Czech Technical University in Prague, Prague, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University in Prague, Prague, Czech Republic","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Erik Derner","orcid":null},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Erik Derner","raw_affiliation_strings":["Czech Technical University in Prague, Prague, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University in Prague, Prague, Czech Republic","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"last","author":{"id":null,"display_name":"Robert Babu\u0161ka","orcid":null},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]},{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["CZ","NL"],"is_corresponding":false,"raw_author_name":"Robert Babu\u0161ka","raw_affiliation_strings":["Delft University of Technology, Delft, The Netherlands and Czech Technical University in Prague, Prague, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Delft, The Netherlands and Czech Technical University in Prague, Prague, Czech Republic","institution_ids":["https://openalex.org/I44504214","https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"958","last_page":"966"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","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/T11975","display_name":"Evolutionary Algorithms and Applications","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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9952999949455261,"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":0.9932000041007996,"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/symbolic-regression","display_name":"Symbolic regression","score":0.6193000078201294},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5608999729156494},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5224000215530396},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5138999819755554},{"id":"https://openalex.org/keywords/symbolic-data-analysis","display_name":"Symbolic data analysis","score":0.46480000019073486},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4153999984264374},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3995000123977661},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.37540000677108765}],"concepts":[{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.6193000078201294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5746999979019165},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5608999729156494},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5224000215530396},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5138999819755554},{"id":"https://openalex.org/C65620979","wikidata":"https://www.wikidata.org/wiki/Q7661176","display_name":"Symbolic data analysis","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4593999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44679999351501465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42640000581741333},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4153999984264374},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.314300000667572},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29440000653266907},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C2776095079","wikidata":"https://www.wikidata.org/wiki/Q489538","display_name":"The Symbolic","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25029999017715454}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3377930.3390152","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3377930.3390152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.11947","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.11947","pdf_url":"https://arxiv.org/pdf/2004.11947","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:tudelft.nl:uuid:0d2ec01c-47a0-4667-857e-4cf13d0a67fe","is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:0d2ec01c-47a0-4667-857e-4cf13d0a67fe","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.11947","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.11947","pdf_url":"https://arxiv.org/pdf/2004.11947","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G176604203","display_name":null,"funder_award_id":"02.1.01/0.0/0.0","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G291036901","display_name":null,"funder_award_id":"CZ.02","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G3490171961","display_name":null,"funder_award_id":"CZ.02.1.01","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G5916014575","display_name":null,"funder_award_id":"CZ.02.1.01/0.0/0.0","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G6594491356","display_name":null,"funder_award_id":"CZ.02.1.01/0.0/0.0/","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G8087443690","display_name":null,"funder_award_id":"CZ.02.1.01/0.0/0.0/15_003/0000470","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"}],"funders":[{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W197747737","https://openalex.org/W1966086707","https://openalex.org/W1979769287","https://openalex.org/W2001095967","https://openalex.org/W2023140507","https://openalex.org/W2046633936","https://openalex.org/W2068823081","https://openalex.org/W2126105956","https://openalex.org/W2526488948","https://openalex.org/W2570076534","https://openalex.org/W2733964674","https://openalex.org/W2783396564","https://openalex.org/W2783456932","https://openalex.org/W2790924949","https://openalex.org/W2892053860","https://openalex.org/W2909661184","https://openalex.org/W2954765142"],"related_works":[],"abstract_inverted_index":{"In":[0],"symbolic":[1,46,89,182],"regression,":[2],"the":[3,13,18,24,31,34,74,97,101,105,107,116,160,167,171,176,192],"search":[4,75],"for":[5,56],"analytic":[6],"models":[7,51,81,134,157],"is":[8,93],"typically":[9],"driven":[10,94],"purely":[11],"by":[12,95,124,184],"prediction":[14,35],"error":[15,36],"observed":[16],"on":[17,131,144],"training":[19,98,161],"data":[20,25,99,129,162],"samples.":[21],"However,":[22],"when":[23],"samples":[26,130],"do":[27],"not":[28,38],"sufficiently":[29],"cover":[30],"input":[32],"space,":[33],"does":[37],"provide":[39],"sufficient":[40],"guidance":[41],"toward":[42],"desired":[43,108,172],"models.":[44],"Standard":[45],"regression":[47,90,183],"techniques":[48],"then":[49],"yield":[50],"that":[52,92,158],"are":[53,121,135],"partially":[54],"incorrect,":[55],"instance,":[57],"in":[58,115,189],"terms":[59,190],"of":[60,104,118,127,170,187,191],"their":[61],"steady-state":[62],"characteristics":[63,174],"or":[64],"local":[65],"behavior.":[66],"If":[67],"these":[68],"properties":[69,106,113],"were":[70],"considered":[71],"already":[72],"during":[73],"process,":[76],"more":[77],"accurate":[78],"and":[79,100],"relevant":[80],"could":[82],"be":[83],"produced.":[84],"We":[85],"propose":[86],"a":[87,125,197],"multi-objective":[88],"approach":[91,140],"both":[96],"prior":[102,168],"knowledge":[103,169],"model":[109,173],"should":[110],"manifest.":[111],"The":[112,138],"given":[114],"form":[117],"formal":[119],"constraints":[120],"internally":[122],"represented":[123],"set":[126],"discrete":[128],"which":[132],"candidate":[133],"exactly":[136],"checked.":[137],"proposed":[139],"was":[141],"experimentally":[142],"evaluated":[143],"three":[145],"test":[146],"problems":[147],"with":[148,166],"results":[149],"clearly":[150],"demonstrating":[151],"its":[152],"capability":[153],"to":[154],"evolve":[155],"realistic":[156],"fit":[159],"well":[163],"while":[164],"complying":[165],"at":[175],"same":[177],"time.":[178],"It":[179],"outperforms":[180],"standard":[181],"several":[185],"orders":[186],"magnitude":[188],"mean":[193],"squared":[194],"deviation":[195],"from":[196],"reference":[198],"model.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2020-05-01T00:00:00"}