{"id":"https://openalex.org/W4390483336","doi":"https://doi.org/10.1109/ssci52147.2023.10372008","title":"Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP","display_name":"Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390483336","doi":"https://doi.org/10.1109/ssci52147.2023.10372008"},"language":"en","primary_location":{"id":"doi:10.1109/ssci52147.2023.10372008","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci52147.2023.10372008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ris.utwente.nl/ws/files/346799492/Using_Reinforcement_Learning_for_Per-Instance_Algorithm_Configuration_on_the_TSP.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026873752","display_name":"Moritz Vinzent Seiler","orcid":"https://orcid.org/0000-0002-1750-9060"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moritz Vinzent Seiler","raw_affiliation_strings":["University of Münster,Data Science: Statistics and Optimization,Münster,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Münster,Data Science: Statistics and Optimization,Münster,Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052640915","display_name":"Jeroen Rook","orcid":"https://orcid.org/0000-0002-3921-0107"},"institutions":[{"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":["NL"],"is_corresponding":false,"raw_author_name":"Jeroen Rook","raw_affiliation_strings":["University of Twente,Data Management and Biometrics,Enschede,Netherlands","Data Management and Biometrics, University of Twente, Enschede, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Twente,Data Management and Biometrics,Enschede,Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Data Management and Biometrics, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008755096","display_name":"Jonathan Heins","orcid":"https://orcid.org/0000-0002-3571-667X"},"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":"Jonathan Heins","raw_affiliation_strings":["TU Dresden,Big Data Analytics in Transportation,Dresden,Germany","Big Data Analytics in Transportation, TU Dresden, Dresden, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Dresden,Big Data Analytics in Transportation,Dresden,Germany","institution_ids":["https://openalex.org/I78650965"]},{"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/A5093631305","display_name":"Oliver Ludger Preu\u00df","orcid":"https://orcid.org/0009-0008-9308-2418"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oliver Ludger Preu\u00df","raw_affiliation_strings":["University of Münster,Data Science: Statistics and Optimization,Münster,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Münster,Data Science: Statistics and Optimization,Münster,Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083504710","display_name":"Jakob Bossek","orcid":"https://orcid.org/0000-0002-4121-4668"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jakob Bossek","raw_affiliation_strings":["Aachen University,Chair for AI Methodology RWTH,Aachen,Germany","Chair for AI Methodology RWTH, Aachen University, Aachen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aachen University,Chair for AI Methodology RWTH,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]},{"raw_affiliation_string":"Chair for AI Methodology RWTH, Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084825454","display_name":"Heike Trautmann","orcid":"https://orcid.org/0000-0002-9788-8282"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heike Trautmann","raw_affiliation_strings":["University of Münster,Data Science: Statistics and Optimization,Münster,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Münster,Data Science: Statistics and Optimization,Münster,Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"361","last_page":"368"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9991999864578247,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9991999864578247,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7464964985847473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7366364002227783},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7342147827148438},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.626479983329773},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6043357253074646},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5764197111129761},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49729540944099426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.486680805683136},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.455686092376709},{"id":"https://openalex.org/keywords/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.44995149970054626},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.429684579372406},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06566467881202698}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7464964985847473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7366364002227783},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7342147827148438},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.626479983329773},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6043357253074646},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5764197111129761},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49729540944099426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.486680805683136},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.455686092376709},{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.44995149970054626},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.429684579372406},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06566467881202698},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ssci52147.2023.10372008","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci52147.2023.10372008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:ris.utwente.nl:publications/dbdcf8c9-6e11-4322-9cbe-43ba89c743a7","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/dbdcf8c9-6e11-4322-9cbe-43ba89c743a7","pdf_url":"https://ris.utwente.nl/ws/files/346799492/Using_Reinforcement_Learning_for_Per-Instance_Algorithm_Configuration_on_the_TSP.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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Seiler, M V, Rook, J, Heins, J, Preu\u00df, O L, Bossek, J & Trautmann, H 2023, Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. in 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence, vol. 2023, IEEE, Piscataway, NJ, pp. 361-368, IEEE Symposium Series on Computational Intelligence,SSCI 2023, Mexico City, Mexico, 5/12/23. https://doi.org/10.1109/SSCI52147.2023.10372008","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:publications/dbdcf8c9-6e11-4322-9cbe-43ba89c743a7","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/dbdcf8c9-6e11-4322-9cbe-43ba89c743a7","pdf_url":"https://ris.utwente.nl/ws/files/346799492/Using_Reinforcement_Learning_for_Per-Instance_Algorithm_Configuration_on_the_TSP.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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Seiler, M V, Rook, J, Heins, J, Preu\u00df, O L, Bossek, J & Trautmann, H 2023, Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. in 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence, vol. 2023, IEEE, Piscataway, NJ, pp. 361-368, IEEE Symposium Series on Computational Intelligence,SSCI 2023, Mexico City, Mexico, 5/12/23. https://doi.org/10.1109/SSCI52147.2023.10372008","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390483336.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W19784850","https://openalex.org/W60686164","https://openalex.org/W74931915","https://openalex.org/W106867050","https://openalex.org/W331118651","https://openalex.org/W1490176645","https://openalex.org/W1991214562","https://openalex.org/W2007747719","https://openalex.org/W2017708378","https://openalex.org/W2041731592","https://openalex.org/W2059409198","https://openalex.org/W2091565802","https://openalex.org/W2137042221","https://openalex.org/W2194775991","https://openalex.org/W2505019202","https://openalex.org/W2734645630","https://openalex.org/W2736601468","https://openalex.org/W2747329762","https://openalex.org/W2807661575","https://openalex.org/W2842884756","https://openalex.org/W2898873842","https://openalex.org/W2917154777","https://openalex.org/W2964281761","https://openalex.org/W3000128672","https://openalex.org/W3013630346","https://openalex.org/W3029372092","https://openalex.org/W3183924758","https://openalex.org/W3210735129","https://openalex.org/W4286970057","https://openalex.org/W4292103359","https://openalex.org/W4306889144","https://openalex.org/W4313395123","https://openalex.org/W4385750389","https://openalex.org/W6731370813","https://openalex.org/W6741002519","https://openalex.org/W6757817989","https://openalex.org/W6800546666"],"related_works":["https://openalex.org/W3159037256","https://openalex.org/W1482441085","https://openalex.org/W2966858528","https://openalex.org/W2975368677","https://openalex.org/W3025823153","https://openalex.org/W2151687600","https://openalex.org/W3136451934","https://openalex.org/W3200006077","https://openalex.org/W2170228269","https://openalex.org/W4297427155"],"abstract_inverted_index":{"Automated":[0],"Algorithm":[1,56,145],"Configuration":[2,57,146],"(AAC)":[3],"usually":[4],"takes":[5],"a":[6,11,20,24,47,69,109],"global":[7],"perspective:":[8],"it":[9,82],"identifies":[10],"parameter":[12],"configuration":[13],"for":[14,86,91,98],"an":[15],"(optimization)":[16],"algorithm":[17],"that":[18,73,151],"maximizes":[19],"performance":[21],"metric":[22],"over":[23],"set":[25,111,127,164],"of":[26,32,54,112,131],"instances.":[27],"However,":[28],"the":[29,37,52,87,92,124,129,139,160],"optimal":[30],"choice":[31],"parameters":[33],"strongly":[34],"depends":[35],"on":[36,46,76,158],"instance":[38,163,167],"at":[39],"hand":[40],"and":[41,165],"should":[42],"thus":[43],"be":[44],"calculated":[45],"per-instance":[48],"basis.":[49],"We":[50,80,134],"explore":[51],"potential":[53],"Per-Instance":[55],"(PIAC)":[58],"by":[59],"using":[60],"Reinforcement":[61],"Learning":[62],"(RL).":[63],"To":[64,102],"this":[65,156],"end,":[66],"we":[67,107],"propose":[68],"novel":[70],"PIAC":[71,105,153],"approach":[72,137,154],"is":[74],"based":[75],"deep":[77],"neural":[78],"networks.":[79],"apply":[81],"to":[83,128,138],"predict":[84],"configurations":[85],"Lin-Kernighan":[88],"heuristic":[89],"(LKH)":[90],"Traveling":[93],"Salesperson":[94],"Problem":[95],"(TSP)":[96],"individually":[97],"every":[99],"single":[100],"instance.":[101],"train":[103],"our":[104,132,136,152],"approach,":[106],"create":[108],"large":[110],"100":[113],"000":[114,119],"TSP":[115],"instances":[116],"with":[117],"2":[118],"nodes":[120],"each":[121],"-":[122],"currently":[123],"largest":[125],"benchmark":[126],"best":[130],"knowledge.":[133],"compare":[135],"state-of-the-art":[140],"AAC":[141],"method":[142],"Sequential":[143],"Model-based":[144],"(SMAC).":[147],"The":[148],"results":[149],"show":[150],"outperforms":[155],"baseline":[157],"both":[159],"newly":[161],"created":[162],"established":[166],"sets.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}