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Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem

Miranda, Enrico S., Fabris, Fabio, Nascimento, Chrystian G. M., Freitas, Alex A., Oliveira, Alexandre C. M. (2018) Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem. In: IEEE Conference on Intelligent Systems. 2018 7th Brazilian Conference on Intelligent Systems (BRACIS). . pp. 169-174. IEEE, USA ISBN 978-1-5386-8023-0. (doi:10.1109/BRACIS.2018.00037)

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https://doi.org/10.1109/BRACIS.2018.00037

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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/BRACIS.2018.00037
Divisions: Faculties > Sciences > School of Computing
Depositing User: F. Fabris
Date Deposited: 30 Apr 2019 11:21 UTC
Last Modified: 03 Jun 2019 09:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73694 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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