About: Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : demo.openlinksw.com associated with source document(s)

Attributes	Values
type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #73694 Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem
sameAs	Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem
http://eprints.org/ontology/hasAccepted	Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (PDF)
http://eprints.org/ontology/hasDocument	Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (PDF) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (PDF) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (Other)
http://eprints.org/ontology/hasPublished	Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (PDF)
dc:hasVersion	Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (PDF) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem (PDF)
Title	Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-12-17
Creator	Enrico S. Miranda Chrystian G. M. Nascimento A. Freitas Fabio Fabris Alexandre C. M. Oliveira
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	IEEE Conference on Intelligent Systems https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/73694#authors
presented at	2018 7th Brazilian Conference on Intelligent Systems (BRACIS)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #73694 Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 31 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software