This HTML5 document contains 39 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n11https://kar.kent.ac.uk/id/eprint/73694#
n2https://kar.kent.ac.uk/id/eprint/
wdrshttp://www.w3.org/2007/05/powder-s#
dchttp://purl.org/dc/elements/1.1/
n4http://purl.org/ontology/bibo/status/
rdfshttp://www.w3.org/2000/01/rdf-schema#
n21doi:10.1109/
n14https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/
n7http://eprints.org/ontology/
n17https://kar.kent.ac.uk/id/event/
bibohttp://purl.org/ontology/bibo/
n13https://kar.kent.ac.uk/id/publication/
n12https://kar.kent.ac.uk/id/org/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n19https://kar.kent.ac.uk/73694/
owlhttp://www.w3.org/2002/07/owl#
n9https://kar.kent.ac.uk/id/document/
n16https://kar.kent.ac.uk/id/
xsdhhttp://www.w3.org/2001/XMLSchema#
n6https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/
n10https://kar.kent.ac.uk/id/person/

Statements

Subject Item
n2:73694
rdf:type
n7:ConferenceItemEPrint n7:EPrint bibo:AcademicArticle bibo:Article
rdfs:seeAlso
n19:
owl:sameAs
n21:BRACIS.2018.00037
n7:hasAccepted
n9:3175552
n7:hasDocument
n9:3175504 n9:3175511 n9:3175512 n9:3175513 n9:3175514 n9:3175515 n9:3175552 n9:3175558 n9:3175559 n9:3175560 n9:3175561 n9:3175562
n7:hasPublished
n9:3175504
dc:hasVersion
n9:3175552 n9:3175504
dcterms:title
Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem
wdrs:describedby
n6:export_kar_RDFN3.n3 n14:export_kar_RDFN3.n3
dcterms:date
2018-12-17
dcterms:creator
n10:ext-9df8394773670e25b74cdb6e17eb1002 n10:ext-820ce3528b88e62e49cb89f310ce122b n10:ext-f33cecf5d2c002405bdf1f3675f497ee n10:ext-f.fabris@kent.ac.uk n10:ext-a.a.freitas@kent.ac.uk
bibo:status
n4:peerReviewed n4:published
dcterms:publisher
n12:ext-af0a9a5baed87c407844a3f5db44597c
bibo:abstract
It is of great interest to build recommendation systems capable of choosing the best solver for a particular problem of a combinatorial optimisation task given past runs of solvers in various problems of that optimisation task. In this paper, a meta-learning approach is proposed to predict which metaheuristic is the best solver for MaxSAT problems. The proposal includes the creation of new meta-features derived from graph descriptions of MaxSAT problems and an interpretation of the meta-model. Our approach successfully selected the best metaheuristic to solve each problem in 87% of the cases. Also, the new meta-features have shown to be as good as the state-of-the-art meta-features, and the meta-model interpretation found interesting problem-specific knowledge.
dcterms:isPartOf
n13:ext-da0463636d7d689525e6df27e89f7a6e n16:repository
bibo:authorList
n11:authors
bibo:presentedAt
n17:ext-1ab6e7cc823f3da666c57d4092a319be