{"id":"https://openalex.org/W4385768231","doi":"https://doi.org/10.24963/ijcai.2023/621","title":"Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems","display_name":"Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385768231","doi":"https://doi.org/10.24963/ijcai.2023/621"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/621","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/621","pdf_url":"https://www.ijcai.org/proceedings/2023/0621.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0621.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103036649","display_name":"Mingyu Huang","orcid":"https://orcid.org/0000-0003-2829-8673"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyu Huang","raw_affiliation_strings":["University of Electronic Science and Technology of China","Glasgow College, University of Electronic Science and Technology of China,, 611731, Chengdu, China","School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Glasgow College, University of Electronic Science and Technology of China,, 611731, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343450","display_name":"Ke Li","orcid":"https://orcid.org/0000-0001-7200-4244"},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ke Li","raw_affiliation_strings":["University of Exeter","Department of Computer Science, University of Exeter, EX4 4QF, Exeter, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter","institution_ids":["https://openalex.org/I23923803"]},{"raw_affiliation_string":"Department of Computer Science, University of Exeter, EX4 4QF, Exeter, UK","institution_ids":["https://openalex.org/I23923803"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100343450"],"corresponding_institution_ids":["https://openalex.org/I23923803"],"apc_list":null,"apc_paid":null,"fwci":0.3633,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65783082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5595","last_page":"5603"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/heuristics","display_name":"Heuristics","score":0.7528349161148071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6342137455940247},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.6122466921806335},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5517638325691223},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.5098928213119507},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.509224534034729},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.47261738777160645},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45402365922927856},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.44604459404945374},{"id":"https://openalex.org/keywords/fitness-landscape","display_name":"Fitness landscape","score":0.41666799783706665},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3613315224647522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35662543773651123},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3482894003391266},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3393515348434448},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2293092906475067},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.10189494490623474}],"concepts":[{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7528349161148071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6342137455940247},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.6122466921806335},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5517638325691223},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.5098928213119507},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.509224534034729},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.47261738777160645},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45402365922927856},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.44604459404945374},{"id":"https://openalex.org/C91852762","wikidata":"https://www.wikidata.org/wiki/Q3307742","display_name":"Fitness landscape","level":3,"score":0.41666799783706665},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3613315224647522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35662543773651123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3482894003391266},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3393515348434448},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2293092906475067},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.10189494490623474},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2023/621","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/621","pdf_url":"https://www.ijcai.org/proceedings/2023/0621.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/621","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/621","pdf_url":"https://www.ijcai.org/proceedings/2023/0621.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1583476792","display_name":"Leveraging Evolutionary Algorithms for Black-Box Adversarial Attacks in Robustness Evaluation of Deep Neural Networks","funder_award_id":"2404317","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G2615012307","display_name":null,"funder_award_id":"62076056","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2679502546","display_name":null,"funder_award_id":"MR/X011135/1","funder_id":"https://openalex.org/F4320314731","funder_display_name":"UK Research and Innovation"},{"id":"https://openalex.org/G6708675553","display_name":null,"funder_award_id":"MR/S017062/1","funder_id":"https://openalex.org/F4320314731","funder_display_name":"UK Research and Innovation"},{"id":"https://openalex.org/G852174929","display_name":null,"funder_award_id":"MR/X011135/1,MR/S017062/1","funder_id":"https://openalex.org/F4320314731","funder_display_name":"UK Research and Innovation"}],"funders":[{"id":"https://openalex.org/F4320314731","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385768231.pdf","grobid_xml":"https://content.openalex.org/works/W4385768231.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W243319620","https://openalex.org/W317693104","https://openalex.org/W1411006099","https://openalex.org/W1762172548","https://openalex.org/W1997676547","https://openalex.org/W2005465418","https://openalex.org/W2024060531","https://openalex.org/W2026729066","https://openalex.org/W2070722739","https://openalex.org/W2071948161","https://openalex.org/W2079505110","https://openalex.org/W2090865599","https://openalex.org/W2094233798","https://openalex.org/W2107709238","https://openalex.org/W2126956247","https://openalex.org/W2147468287","https://openalex.org/W2150337126","https://openalex.org/W2161032864","https://openalex.org/W2307604735","https://openalex.org/W2387462954","https://openalex.org/W2612872092","https://openalex.org/W2613000030","https://openalex.org/W2745335589","https://openalex.org/W2786672974","https://openalex.org/W2887662879","https://openalex.org/W2888078032","https://openalex.org/W2888161290","https://openalex.org/W2959500001","https://openalex.org/W2963512530","https://openalex.org/W2973142703","https://openalex.org/W3094504436","https://openalex.org/W3123511746","https://openalex.org/W4235132546","https://openalex.org/W4239181501","https://openalex.org/W4283717172"],"related_works":["https://openalex.org/W2280422768","https://openalex.org/W3143197806","https://openalex.org/W4252555497","https://openalex.org/W3121175838","https://openalex.org/W3016293053","https://openalex.org/W1690653314","https://openalex.org/W2401723157","https://openalex.org/W2065055572","https://openalex.org/W2084470113","https://openalex.org/W1958774675"],"abstract_inverted_index":{"One":[0],"of":[1,62,89,141,144,161],"the":[2,59,109,123,128,138,142,159],"most":[3],"common":[4],"problem-solving":[5],"heuristics":[6],"is":[7,66,165],"by":[8,81],"analogy.":[9],"For":[10],"a":[11,14,20,29,87,162],"given":[12],"problem,":[13],"solver":[15,30,164],"can":[16],"be":[17,41],"viewed":[18],"as":[19,86,127],"strategic":[21],"walk":[22],"on":[23,154,167],"its":[24],"fitness":[25,47,90],"landscape.":[26],"Thus":[27],"if":[28],"works":[31],"for":[32,43],"one":[33],"problem":[34,126],"instance,":[35],"we":[36,121],"expect":[37],"it":[38,65],"will":[39],"also":[40],"effective":[42],"other":[44],"instances":[45],"whose":[46],"landscapes":[48,148],"essentially":[49],"share":[50],"structural":[51,112,145],"similarities":[52],"with":[53],"each":[54],"other.":[55],"However,":[56],"due":[57],"to":[58,70,95,101,107,136],"black-box":[60],"nature":[61],"combinatorial":[63],"optimization,":[64],"far":[67],"from":[68],"trivial":[69],"infer":[71],"such":[72],"similarity":[73,146],"in":[74,115],"real-world":[75],"scenarios.":[76],"To":[77],"bridge":[78],"this":[79,92],"gap,":[80],"using":[82],"local":[83],"optima":[84],"network":[85],"proxy":[88],"landscapes,":[91],"paper":[93],"proposed":[94],"leverage":[96],"graph":[97],"data":[98],"mining":[99],"techniques":[100],"conduct":[102],"qualitative":[103],"and":[104,130],"quantitative":[105],"analyses":[106],"explore":[108],"latent":[110],"topological":[111],"information":[113],"embedded":[114],"those":[116],"landscapes.":[117,170],"In":[118],"our":[119,131],"experiments,":[120],"use":[122],"number":[124],"partitioning":[125],"case":[129],"empirical":[132],"results":[133],"are":[134],"inspiring":[135],"support":[137],"overall":[139],"assumption":[140],"existence":[143],"between":[147],"within":[149],"neighboring":[150],"dimensions.":[151],"Besides,":[152],"experiments":[153],"simulated":[155],"annealing":[156],"demonstrate":[157],"that":[158],"performance":[160],"meta-heuristic":[163],"similar":[166,169],"structurally":[168]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}