{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1363670319414672000.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1162/evco_a_00215"}},{"identifier":{"@type":"URI","@value":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00215"}}],"dc:title":[{"@value":"Leveraging TSP Solver Complementarity through Machine Learning"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p> The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement. </jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1383670319414672000","@type":"Researcher","foaf:name":[{"@value":"Pascal Kerschke"}],"jpcoar:affiliationName":[{"@value":"Information Systems and Statistics, University of Münster, 48149 Münster, Germany"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670319414672003","@type":"Researcher","foaf:name":[{"@value":"Lars Kotthoff"}],"jpcoar:affiliationName":[{"@value":"Department of Computer Science, University of British Columbia, Vancouver, B.C., V6T 1Z4, Canada"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670319414672001","@type":"Researcher","foaf:name":[{"@value":"Jakob Bossek"}],"jpcoar:affiliationName":[{"@value":"Information Systems and Statistics, University of Münster, 48149 Münster, Germany"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670319414672002","@type":"Researcher","foaf:name":[{"@value":"Holger H. Hoos"}],"jpcoar:affiliationName":[{"@value":"Department of Computer Science, University of British Columbia, Vancouver, B.C., V6T 1Z4, Canada"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670319414672004","@type":"Researcher","foaf:name":[{"@value":"Heike Trautmann"}],"jpcoar:affiliationName":[{"@value":"Information Systems and Statistics, University of Münster, 48149 Münster, Germany"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"10636560"},{"@type":"EISSN","@value":"15309304"}],"prism:publicationName":[{"@value":"Evolutionary Computation"}],"dc:publisher":[{"@value":"MIT Press - Journals"}],"prism:publicationDate":"2018-12","prism:volume":"26","prism:number":"4","prism:startingPage":"597","prism:endingPage":"620"},"reviewed":"false","url":[{"@id":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00215"}],"createdAt":"2017-08-24","modifiedAt":"2021-03-12","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360290617484284416","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1162/evco_a_00215"},{"@type":"CROSSREF","@value":"10.1162/evco_a_00268_references_DOI_9cBFABaR2Uu7AUca5RWuifOkXW8"}]}