{"id":"https://openalex.org/W3046303201","doi":"https://doi.org/10.1145/3377929.3390078","title":"Feature engineering for improving robustness of crossover in symbolic regression","display_name":"Feature engineering for improving robustness of crossover in symbolic regression","publication_year":2020,"publication_date":"2020-07-08","ids":{"openalex":"https://openalex.org/W3046303201","doi":"https://doi.org/10.1145/3377929.3390078","mag":"3046303201"},"language":"en","primary_location":{"id":"doi:10.1145/3377929.3390078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3377929.3390078","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 Companion","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/A5081238367","display_name":"Aliyu Sani Sambo","orcid":"https://orcid.org/0000-0001-7069-1933"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Aliyu Sani Sambo","raw_affiliation_strings":["Birmingham City University, UK"],"affiliations":[{"raw_affiliation_string":"Birmingham City University, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054830021","display_name":"R. Muhammad Atif Azad","orcid":"https://orcid.org/0000-0002-4013-5415"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"R. Muhammad Atif Azad","raw_affiliation_strings":["Birmingham City University, UK"],"affiliations":[{"raw_affiliation_string":"Birmingham City University, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031619356","display_name":"Yevgeniya Kovalchuk","orcid":"https://orcid.org/0000-0003-4306-4680"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yevgeniya Kovalchuk","raw_affiliation_strings":["Birmingham City University, UK"],"affiliations":[{"raw_affiliation_string":"Birmingham City University, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001701823","display_name":"Vivek Padmanaabhan Indramohan","orcid":null},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vivek Padmanaabhan Indramohan","raw_affiliation_strings":["Birmingham City University, UK"],"affiliations":[{"raw_affiliation_string":"Birmingham City University, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101441856","display_name":"Hanifa Shah","orcid":"https://orcid.org/0000-0001-6289-9160"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hanifa Shah","raw_affiliation_strings":["Birmingham City University, UK"],"affiliations":[{"raw_affiliation_string":"Birmingham City University, UK","institution_ids":["https://openalex.org/I12870472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5081238367"],"corresponding_institution_ids":["https://openalex.org/I12870472"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53703381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"249","last_page":"250"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":1.0,"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":1.0,"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":0.9948999881744385,"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":0.9916999936103821,"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/crossover","display_name":"Crossover","score":0.9117985963821411},{"id":"https://openalex.org/keywords/symbolic-regression","display_name":"Symbolic regression","score":0.901465654373169},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.8859850168228149},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.740872323513031},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6572639346122742},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5913151502609253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4908216893672943},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.46373429894447327},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4633387327194214},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.453536719083786},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.4505149722099304},{"id":"https://openalex.org/keywords/fitness-approximation","display_name":"Fitness approximation","score":0.4126870930194855},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.3961680829524994},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3920256197452545},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32833677530288696},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2922767996788025},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2875264883041382},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25028809905052185}],"concepts":[{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.9117985963821411},{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.901465654373169},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.8859850168228149},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.740872323513031},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6572639346122742},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5913151502609253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4908216893672943},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.46373429894447327},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4633387327194214},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.453536719083786},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.4505149722099304},{"id":"https://openalex.org/C148392497","wikidata":"https://www.wikidata.org/wiki/Q16250539","display_name":"Fitness approximation","level":4,"score":0.4126870930194855},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.3961680829524994},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3920256197452545},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32833677530288696},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2922767996788025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2875264883041382},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25028809905052185},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3377929.3390078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3377929.3390078","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 Companion","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":4,"referenced_works":["https://openalex.org/W1581584868","https://openalex.org/W1827996529","https://openalex.org/W2146565186","https://openalex.org/W2157817892"],"related_works":["https://openalex.org/W2968285896","https://openalex.org/W1593354505","https://openalex.org/W1963986408","https://openalex.org/W2046401452","https://openalex.org/W1792573625","https://openalex.org/W2561856575","https://openalex.org/W1479817123","https://openalex.org/W1925901016","https://openalex.org/W3046303201","https://openalex.org/W1562117546"],"abstract_inverted_index":{"Isolating":[0],"the":[1,20,46,56,70,77,102,113,134,140,155,159,162,169,174,178],"fitness-contribution":[2,71],"of":[3,45,58,72,80,86,89,104,115,161,168,171,180],"substructures":[4,16,74],"is":[5,29],"typically":[6,35],"a":[7,84],"difficult":[8],"task":[9],"in":[10,32],"Genetic":[11],"Programming":[12],"(GP).":[13],"Hence,":[14],"useful":[15],"are":[17],"lost":[18],"when":[19],"overall":[21],"structure":[22],"(model)":[23],"performs":[24],"poorly.":[25],"Furthermore,":[26],"while":[27],"crossover":[28,81],"heavily":[30],"used":[31],"GP,":[33],"it":[34],"produces":[36],"offspring":[37],"models":[38,122],"with":[39,143,148],"significantly":[40,176],"lower":[41],"fitness":[42,135,182],"than":[43],"that":[44,154],"parents.":[47],"In":[48],"symbolic":[49],"regression,":[50],"this":[51],"degradation":[52],"also":[53],"occurs":[54],"because":[55],"coefficients":[57,103],"an":[59,116],"evolving":[60,83],"model":[61],"lose":[62],"utility":[63],"after":[64],"crossover.":[65],"This":[66],"paper":[67,138],"proposes":[68],"isolating":[69],"various":[73],"and":[75,146],"reducing":[76],"negative":[78],"impact":[79],"by":[82],"set":[85],"features":[87,110],"instead":[88],"monolithic":[90],"models.":[91],"The":[92,137],"method":[93,142,157,175],"then":[94],"leverages":[95],"multiple":[96],"linear":[97,149],"regression":[98],"(MLR)":[99],"to":[100],"optimise":[101],"these":[105],"features.":[106],"Since":[107],"adding":[108],"new":[109],"cannot":[111],"degrade":[112],"accuracy":[114,160],"MLR":[117],"produced":[118],"model,":[119],"MLR-aided":[120],"GP":[121,145,147],"can":[123],"bloat.":[124],"To":[125],"penalise":[126],"such":[127],"additions,":[128],"we":[129],"use":[130],"Adjusted":[131],"R2":[132],"as":[133],"function.":[136],"compares":[139],"proposed":[141,156],"standard":[144],"scaling.":[150],"Experimental":[151],"results":[152],"show":[153],"matches":[158],"competing":[163],"methods":[164],"within":[165],"only":[166],"1/10th":[167],"number":[170],"generations.":[172],"Also,":[173],"decreases":[177],"rate":[179],"post-crossover":[181],"degradation.":[183]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}