{"id":"https://openalex.org/W3083348782","doi":"https://doi.org/10.1109/cec48606.2020.9185716","title":"Fitness Landscape Analysis Metrics based on Sobol Indices and Fitness- and State-Distributions","display_name":"Fitness Landscape Analysis Metrics based on Sobol Indices and Fitness- and State-Distributions","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3083348782","doi":"https://doi.org/10.1109/cec48606.2020.9185716","mag":"3083348782"},"language":"en","primary_location":{"id":"doi:10.1109/cec48606.2020.9185716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5022938610","display_name":"Christoph Waibel","orcid":"https://orcid.org/0000-0001-6077-1411"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Christoph Waibel","raw_affiliation_strings":["Architecture and Building Systems, Inst. of Technology in Architecture ETH Zurich, Zurich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Architecture and Building Systems, Inst. of Technology in Architecture ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054347084","display_name":"Georgios Mavromatidis","orcid":"https://orcid.org/0000-0003-0227-4518"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Georgios Mavromatidis","raw_affiliation_strings":["Dept. of Management, Technology, and Ec. ETH Zurich, Group for Sustainability and Technology, Zurich, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Management, Technology, and Ec. ETH Zurich, Group for Sustainability and Technology, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100675816","display_name":"Yongwei Zhang","orcid":"https://orcid.org/0000-0003-3381-6340"},"institutions":[{"id":"https://openalex.org/I4210096899","display_name":"Jiangsu University of Science and Technology","ror":"https://ror.org/00tyjp878","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096899"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong-Wei Zhang","raw_affiliation_strings":["College of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China","institution_ids":["https://openalex.org/I4210096899"]}]}],"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":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9984999895095825,"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":0.9984999895095825,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9890000224113464,"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/sobol-sequence","display_name":"Sobol sequence","score":0.8673380613327026},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6704666614532471},{"id":"https://openalex.org/keywords/skewness","display_name":"Skewness","score":0.574247419834137},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5601422190666199},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5005836486816406},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4878282845020294},{"id":"https://openalex.org/keywords/distance-correlation","display_name":"Distance correlation","score":0.4532318413257599},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4472101330757141},{"id":"https://openalex.org/keywords/fitness-landscape","display_name":"Fitness landscape","score":0.43665021657943726},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.43533855676651},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.424746572971344},{"id":"https://openalex.org/keywords/fitness-approximation","display_name":"Fitness approximation","score":0.4128607213497162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3050912618637085},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.2737840712070465},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2375115156173706},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.23617595434188843},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09849363565444946}],"concepts":[{"id":"https://openalex.org/C49740808","wikidata":"https://www.wikidata.org/wiki/Q7550116","display_name":"Sobol sequence","level":3,"score":0.8673380613327026},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6704666614532471},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.574247419834137},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5601422190666199},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5005836486816406},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4878282845020294},{"id":"https://openalex.org/C121694360","wikidata":"https://www.wikidata.org/wiki/Q5282862","display_name":"Distance correlation","level":3,"score":0.4532318413257599},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4472101330757141},{"id":"https://openalex.org/C91852762","wikidata":"https://www.wikidata.org/wiki/Q3307742","display_name":"Fitness landscape","level":3,"score":0.43665021657943726},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.43533855676651},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.424746572971344},{"id":"https://openalex.org/C148392497","wikidata":"https://www.wikidata.org/wiki/Q16250539","display_name":"Fitness approximation","level":4,"score":0.4128607213497162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3050912618637085},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2737840712070465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2375115156173706},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.23617595434188843},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09849363565444946},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec48606.2020.9185716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W104877095","https://openalex.org/W243319620","https://openalex.org/W1597366847","https://openalex.org/W1933385826","https://openalex.org/W1973492181","https://openalex.org/W2039906176","https://openalex.org/W2044400415","https://openalex.org/W2063966510","https://openalex.org/W2079604837","https://openalex.org/W2088765131","https://openalex.org/W2101589741","https://openalex.org/W2119401655","https://openalex.org/W2126328232","https://openalex.org/W2137702546","https://openalex.org/W2141119792","https://openalex.org/W2171263583","https://openalex.org/W2182761707","https://openalex.org/W2185575900","https://openalex.org/W2219036994","https://openalex.org/W2517173125","https://openalex.org/W2921232026","https://openalex.org/W2950680102","https://openalex.org/W2967435317","https://openalex.org/W2967896732","https://openalex.org/W2986597606","https://openalex.org/W3011730451","https://openalex.org/W4285719527","https://openalex.org/W6640368482","https://openalex.org/W6685120113"],"related_works":["https://openalex.org/W2036107212","https://openalex.org/W2164084664","https://openalex.org/W2159937791","https://openalex.org/W1881607630","https://openalex.org/W1591022951","https://openalex.org/W2138958036","https://openalex.org/W3096859156","https://openalex.org/W4250294025","https://openalex.org/W1966735857","https://openalex.org/W2063966510"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"new":[3],"Fitness":[4],"Landscape":[5],"Analysis":[6],"(FLA)":[7],"metrics":[8,22,43,75,111,132],"as":[9],"measures":[10],"for":[11,58,100],"problem":[12],"difficulty":[13],"in":[14,31,64,113],"heuristic":[15],"search.":[16],"The":[17,34],"sensitivity":[18,32],"and":[19,36,38,41,49,67,70,84,97],"variable":[20],"interaction":[21],"are":[23,44],"based":[24,45],"on":[25,46],"Sobol":[26],"indices,":[27],"a":[28],"common":[29],"technique":[30],"analysis.":[33],"fitness-":[35,40],"state-variance":[37],"the":[39,47,130],"state-skewness":[42],"second":[48],"third":[50],"central":[51],"statistical":[52,91],"moments.":[53],"We":[54],"compute":[55],"metric":[56],"values":[57],"around":[59],"550":[60],"continuous":[61],"test":[62],"functions":[63],"2,":[65],"5":[66],"10":[68],"dimensions":[69],"compare":[71],"it":[72],"to":[73],"well-established":[74],"(Fitness":[76],"Distance":[77],"Correlation,":[78],"Autocorrelation,":[79],"Information":[80,83,86],"Content,":[81],"Density-Basin":[82],"Partial":[85],"Content).":[87],"By":[88],"conducting":[89],"two-sample":[90],"tests":[92],"(T-Test,":[93],"F-Test,":[94],"Kolmogorov-Smirnov":[95],"Test,":[96],"Rank-Sum":[98],"Test)":[99],"all":[101],"combinations":[102],"of":[103],"FLA":[104],"metrics,":[105],"we":[106,118],"demonstrate":[107],"that":[108,121,133],"our":[109],"proposed":[110],"result":[112],"significantly":[114],"different":[115],"distributions.":[116],"Thus,":[117],"can":[119],"conclude":[120],"they":[122],"reveal":[123],"fitness":[124],"landscape":[125],"characteristics":[126],"not":[127],"captured":[128],"by":[129],"existing":[131],"were":[134],"considered.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
