{"id":"https://openalex.org/W4291270683","doi":"https://doi.org/10.1007/978-3-031-14714-2_40","title":"HPO $$\\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by\u00a0Means of\u00a0Exploratory Landscape Analysis","display_name":"HPO $$\\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by\u00a0Means of\u00a0Exploratory Landscape Analysis","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4291270683","doi":"https://doi.org/10.1007/978-3-031-14714-2_40"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-031-14714-2_40","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-14714-2_40","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-14714-2_40.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-14714-2_40.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084443103","display_name":"Lennart Schneider","orcid":"https://orcid.org/0000-0003-4152-5308"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Lennart Schneider","raw_affiliation_strings":["Chair of Statistical Learning and Data Science, LMU Munich, Munich, Germany"],"raw_orcid":"https://orcid.org/0000-0003-4152-5308","affiliations":[{"raw_affiliation_string":"Chair of Statistical Learning and Data Science, LMU Munich, Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024294898","display_name":"Lennart Sch\u00e4permeier","orcid":"https://orcid.org/0000-0003-3929-7465"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"Technische Universit\u00e4t Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lennart Sch\u00e4permeier","raw_affiliation_strings":["Big Data Analytics in Transportation, TU Dresden, Dresden, Germany"],"raw_orcid":"https://orcid.org/0000-0003-3929-7465","affiliations":[{"raw_affiliation_string":"Big Data Analytics in Transportation, TU Dresden, Dresden, Germany","institution_ids":["https://openalex.org/I78650965"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011446626","display_name":"Raphael Patrick Prager","orcid":"https://orcid.org/0000-0003-1237-4248"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Raphael Patrick Prager","raw_affiliation_strings":["Data Science: Statistics and Optimization, University of M\u00fcnster, M\u00fcnster, Germany"],"raw_orcid":"https://orcid.org/0000-0003-1237-4248","affiliations":[{"raw_affiliation_string":"Data Science: Statistics and Optimization, University of M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069072700","display_name":"Bernd Bischl","orcid":"https://orcid.org/0000-0001-6002-6980"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bernd Bischl","raw_affiliation_strings":["Chair of Statistical Learning and Data Science, LMU Munich, Munich, Germany"],"raw_orcid":"https://orcid.org/0000-0001-6002-6980","affiliations":[{"raw_affiliation_string":"Chair of Statistical Learning and Data Science, LMU Munich, Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084825454","display_name":"Heike Trautmann","orcid":"https://orcid.org/0000-0002-9788-8282"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]},{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["DE","NL"],"is_corresponding":false,"raw_author_name":"Heike Trautmann","raw_affiliation_strings":["Data Management and Biometrics Group, University of Twente, Enschede, Netherlands","Data Science: Statistics and Optimization, University of M\u00fcnster, M\u00fcnster, Germany"],"raw_orcid":"https://orcid.org/0000-0002-9788-8282","affiliations":[{"raw_affiliation_string":"Data Management and Biometrics Group, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Data Science: Statistics and Optimization, University of M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008455153","display_name":"Pascal Kerschke","orcid":"https://orcid.org/0000-0003-2862-1418"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"Technische Universit\u00e4t Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pascal Kerschke","raw_affiliation_strings":["Big Data Analytics in Transportation, TU Dresden, Dresden, Germany","Technische Universit\u00e4t Dresden, Dresden, Germany"],"raw_orcid":"https://orcid.org/0000-0003-2862-1418","affiliations":[{"raw_affiliation_string":"Big Data Analytics in Transportation, TU Dresden, Dresden, Germany","institution_ids":["https://openalex.org/I78650965"]},{"raw_affiliation_string":"Technische Universit\u00e4t Dresden, Dresden, Germany","institution_ids":["https://openalex.org/I78650965"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5084443103"],"corresponding_institution_ids":["https://openalex.org/I8204097"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":1.3694,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.8356554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"575","last_page":"589"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","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/T12535","display_name":"Machine Learning and Data Classification","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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9994000196456909,"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":0.9958999752998352,"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/benchmark","display_name":"Benchmark (surveying)","score":0.771192193031311},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7543247938156128},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7323766946792603},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5521742105484009},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5183137655258179},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.48119717836380005},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47532275319099426}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.771192193031311},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7543247938156128},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7323766946792603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5521742105484009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5183137655258179},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.48119717836380005},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47532275319099426},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/978-3-031-14714-2_40","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-14714-2_40","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-14714-2_40.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},{"id":"pmh:oai:ris.utwente.nl:publications/6227d6c4-e529-4b4b-a768-af66fce8f6c0","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/6227d6c4-e529-4b4b-a768-af66fce8f6c0","pdf_url":"https://ris.utwente.nl/ws/files/355349520/978-3-031-14714-2_40.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Schneider, L, Sch\u00e4permeier, L, Prager, R P, Bischl, B, Trautmann, H & Kerschke, P 2022, HPO \u00d7 ELA : Investigating Hyperparameter Optimization Landscapes by\u00a0Means of\u00a0Exploratory Landscape Analysis. in G Rudolph, A V Kononova, H Aguirre, P Kerschke, G Ochoa & T Tu\u0161ar (eds), Parallel Problem Solving from Nature \u2013 PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10\u201314, 2022, Proceedings, Part I. Lecture Notes in Computer Science, vol. 13398, Springer, Cham, pp. 575-589, 17th International Conference on Parallel Problem Solving from Nature, PPSN 2022, Dortmund, Germany, 10/09/22. https://doi.org/10.1007/978-3-031-14714-2_40","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/978-3-031-14714-2_40","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-14714-2_40","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-14714-2_40.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3467264548","display_name":null,"funder_award_id":"01IS18036A","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"},{"id":"https://openalex.org/G8010892114","display_name":null,"funder_award_id":"20-3410-2-9-8","funder_id":"https://openalex.org/F4320331012","funder_display_name":"Bayerische Staatsministerium f\u00fcr Wirtschaft, Landesentwicklung und Energie"}],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"},{"id":"https://openalex.org/F4320331012","display_name":"Bayerische Staatsministerium f\u00fcr Wirtschaft, Landesentwicklung und Energie","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4291270683.pdf","grobid_xml":"https://content.openalex.org/works/W4291270683.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1510052597","https://openalex.org/W1945195796","https://openalex.org/W1972278817","https://openalex.org/W1997807546","https://openalex.org/W2003118470","https://openalex.org/W2050711624","https://openalex.org/W2063966510","https://openalex.org/W2095314857","https://openalex.org/W2102539288","https://openalex.org/W2107230221","https://openalex.org/W2132862423","https://openalex.org/W2150337126","https://openalex.org/W2295598076","https://openalex.org/W2732736182","https://openalex.org/W2750236386","https://openalex.org/W2768617563","https://openalex.org/W2786029688","https://openalex.org/W2854334010","https://openalex.org/W2888079457","https://openalex.org/W2952369323","https://openalex.org/W2996717911","https://openalex.org/W3015201913","https://openalex.org/W3040348070","https://openalex.org/W3107783396","https://openalex.org/W3151399059","https://openalex.org/W4213308398","https://openalex.org/W4294237904","https://openalex.org/W6768133545"],"related_works":["https://openalex.org/W4295309597","https://openalex.org/W3081580854","https://openalex.org/W4287683259","https://openalex.org/W4210794429","https://openalex.org/W4223456145","https://openalex.org/W4309113015","https://openalex.org/W3199608561","https://openalex.org/W4283697347","https://openalex.org/W3155731460","https://openalex.org/W4287121057"],"abstract_inverted_index":{"Abstract":[0],"Hyperparameter":[1],"optimization":[2,46,68,124],"(HPO)":[3],"is":[4,107,196],"a":[5,53,170,175],"key":[6],"component":[7],"of":[8,43,55,66,77,88,96,112,146,177,203,210,218],"machine":[9],"learning":[10],"models":[11],"for":[12,22,212],"achieving":[13],"peak":[14],"predictive":[15],"performance.":[16],"While":[17],"numerous":[18],"methods":[19],"and":[20,38,91,135,138,141,149,191,214,221],"algorithms":[21],"HPO":[23,84,134,148,162,185,213],"have":[24],"been":[25,34],"proposed":[26],"over":[27],"the":[28,40,75,97,110,113,122,133,147,161,166,184],"last":[29],"years,":[30],"little":[31],"progress":[32],"has":[33],"made":[35],"in":[36,152,187],"illuminating":[37],"examining":[39],"actual":[41],"structure":[42],"these":[44,200],"black-box":[45,80,123],"problems.":[47,69,205],"Exploratory":[48],"landscape":[49],"analysis":[50,145],"(ELA)":[51],"subsumes":[52],"set":[54],"techniques":[56],"that":[57,180,193],"can":[58],"be":[59],"used":[60],"to":[61,158,165,183],"gain":[62],"knowledge":[63],"about":[64],"properties":[65],"unknown":[67],"In":[70],"this":[71],"paper,":[72],"we":[73],"evaluate":[74],"performance":[76,111,195],"five":[78],"different":[79,103],"optimizers":[81,115],"on":[82,101,117,132,169,199],"30":[83],"problems,":[85],"which":[86],"consist":[87],"two-,":[89],"three-":[90],"five-dimensional":[92],"continuous":[93],"search":[94],"spaces":[95],"XGBoost":[98],"learner":[99],"trained":[100],"10":[102],"data":[104],"sets.":[105],"This":[106],"contrasted":[108],"with":[109],"same":[114],"evaluated":[116],"360":[118],"problem":[119],"instances":[120],"from":[121],"benchmark":[125,204],"(BBOB).":[126],"We":[127,173,206],"then":[128],"compute":[129],"ELA":[130,153,188,211],"features":[131],"BBOB":[136,150,167,178],"problems":[137,151,163,168,179,186],"examine":[139],"similarities":[140],"differences.":[142],"A":[143],"cluster":[144],"feature":[154,189],"space":[155,190],"allows":[156],"us":[157],"identify":[159,174],"how":[160],"compare":[164],"structural":[171],"meta-level.":[172],"subset":[176],"are":[181],"close":[182],"show":[192],"optimizer":[194],"comparably":[197],"similar":[198],"two":[201],"sets":[202],"highlight":[207],"open":[208],"challenges":[209],"discuss":[215],"potential":[216],"directions":[217],"future":[219],"research":[220],"applications.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}