{"id":"https://openalex.org/W2050490708","doi":"https://doi.org/10.1145/1570256.1570432","title":"Fitness landscapes and problem hardness in genetic programming","display_name":"Fitness landscapes and problem hardness in genetic programming","publication_year":2009,"publication_date":"2009-07-08","ids":{"openalex":"https://openalex.org/W2050490708","doi":"https://doi.org/10.1145/1570256.1570432","mag":"2050490708"},"language":"en","primary_location":{"id":"doi:10.1145/1570256.1570432","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1570256.1570432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers","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/A5056916227","display_name":"Leonardo Vanneschi","orcid":"https://orcid.org/0000-0003-4732-3328"},"institutions":[{"id":"https://openalex.org/I66752286","display_name":"University of Milano-Bicocca","ror":"https://ror.org/01ynf4891","country_code":"IT","type":"education","lineage":["https://openalex.org/I66752286"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Leonardo Vanneschi","raw_affiliation_strings":["University of Milano-Bicocca, Milan, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Milano-Bicocca, Milan, Italy","institution_ids":["https://openalex.org/I66752286"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5056916227"],"corresponding_institution_ids":["https://openalex.org/I66752286"],"apc_list":null,"apc_paid":null,"fwci":0.9748,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74845712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3657","last_page":"3684"},"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":0.9988999962806702,"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/T11764","display_name":"Evolution and Genetic Dynamics","score":0.9684000015258789,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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/fitness-landscape","display_name":"Fitness landscape","score":0.8989439010620117},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.8167784214019775},{"id":"https://openalex.org/keywords/fitness-approximation","display_name":"Fitness approximation","score":0.7220703959465027},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.6275527477264404},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.6242628693580627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5874162316322327},{"id":"https://openalex.org/keywords/tabu-search","display_name":"Tabu search","score":0.5871859788894653},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.5822667479515076},{"id":"https://openalex.org/keywords/fitness-proportionate-selection","display_name":"Fitness proportionate selection","score":0.5557764172554016},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.46740394830703735},{"id":"https://openalex.org/keywords/genetic-representation","display_name":"Genetic representation","score":0.44059687852859497},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.43377768993377686},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4236377775669098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4066222012042999},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3824470639228821},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35627782344818115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3212314248085022},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2821369171142578}],"concepts":[{"id":"https://openalex.org/C91852762","wikidata":"https://www.wikidata.org/wiki/Q3307742","display_name":"Fitness landscape","level":3,"score":0.8989439010620117},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.8167784214019775},{"id":"https://openalex.org/C148392497","wikidata":"https://www.wikidata.org/wiki/Q16250539","display_name":"Fitness approximation","level":4,"score":0.7220703959465027},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.6275527477264404},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.6242628693580627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5874162316322327},{"id":"https://openalex.org/C123370116","wikidata":"https://www.wikidata.org/wiki/Q1424540","display_name":"Tabu search","level":2,"score":0.5871859788894653},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.5822667479515076},{"id":"https://openalex.org/C99701942","wikidata":"https://www.wikidata.org/wiki/Q5455479","display_name":"Fitness proportionate selection","level":4,"score":0.5557764172554016},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.46740394830703735},{"id":"https://openalex.org/C6135463","wikidata":"https://www.wikidata.org/wiki/Q5532920","display_name":"Genetic representation","level":3,"score":0.44059687852859497},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.43377768993377686},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4236377775669098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4066222012042999},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3824470639228821},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35627782344818115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3212314248085022},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2821369171142578},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1570256.1570432","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1570256.1570432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W53373509","https://openalex.org/W1487901312","https://openalex.org/W1529900568","https://openalex.org/W1583586846","https://openalex.org/W1589720045","https://openalex.org/W1596643823","https://openalex.org/W1714918242","https://openalex.org/W1894326400","https://openalex.org/W2105306314","https://openalex.org/W2134909045","https://openalex.org/W2136765624","https://openalex.org/W2150918305"],"related_works":["https://openalex.org/W2036107212","https://openalex.org/W2164084664","https://openalex.org/W2159937791","https://openalex.org/W2050206882","https://openalex.org/W2138958036","https://openalex.org/W1591022951","https://openalex.org/W1634842376","https://openalex.org/W1571420718","https://openalex.org/W1622672652","https://openalex.org/W192154461"],"abstract_inverted_index":{"The":[0,101],"performance":[1],"of":[2,28,50,66,71,82,93,103,111,120,145,151,182,188],"searching":[3,155],"agents,":[4],"or":[5,15],"metaheuristics,":[6],"like":[7],"evolutionary":[8],"algorithms":[9,18],"(genetics":[10],"algorithms,":[11],"genetic":[12,73,205],"programming,":[13],"etc.)":[14,23],"local":[16],"search":[17,30,41],"(simulated":[19],"annealing,":[20],"tabu":[21],"search,":[22],"depend":[24],"on":[25,123],"some":[26,79,110,191,217],"properties":[27],"the":[29,40,44,48,64,67,72,91,112,118,131,149,186,201,209],"space":[31,42],"structure.":[32],"One":[33],"concept":[34,92,102,187],"that":[35,147,197],"allows":[36],"us":[37],"to":[38,89,96,171,185],"analyse":[39],"is":[43,58],"fitness":[45,56,83,94,134,159,221],"landscape.":[46,84],"In":[47],"case":[49],"Genetic":[51,97,121],"Programming,":[52,98],"defining":[53],"and":[54,109,117,136,142,190],"handling":[55],"landscapes":[57,125,135,222],"a":[59,143,152,158,180],"particularly":[60],"hard":[61],"task,":[62],"given":[63,170],"complexity":[65],"structures":[68],"being":[69],"evolved":[70],"operators":[74,206],"used.":[75],"This":[76],"tutorial":[77],"presents":[78],"general":[80],"definitions":[81],"Subsequently,":[85],"we":[86],"will":[87,106,126,139,168],"try":[88],"instantiate":[90],"landscape":[95,115,160],"discussing":[99],"problems.":[100],"landcsape":[104],"geometry":[105],"be":[107,127,140,169],"introduced":[108],"most":[113,202],"common":[114],"geometries":[116],"dynamics":[119],"Programming":[122],"those":[124,164],"discussed.":[128,224],"After":[129],"that,":[130],"binding":[132],"between":[133],"problem":[137],"difficulty":[138,150],"discussed":[141],"set":[144,181],"measures":[146,183,196],"characterize":[148],"metaheuristic":[153],"in":[154,157],"solutions":[156],"are":[161,198,223],"analysed.":[162],"Among":[163],"measures,":[165],"particular":[166,208],"relevance":[167],"Fitness":[172],"Distance":[173],"Correlation":[174],"(FDC),":[175],"Negative":[176],"Slope":[177],"Coefficient":[178],"(NSC),":[179],"bound":[184],"Neutrality":[189],"distance":[192],"metrics":[193],"and/or":[194],"similarity":[195],"consistent":[199],"with":[200],"commonly":[203],"used":[204],"(in":[207],"recently":[210],"defined":[211],"subtree":[212],"crossover":[213],"based":[214],"distance).":[215],"Finally,":[216],"open":[218],"questions":[219],"about":[220]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}