Portfolio approaches for constraint optimization problems

Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Within the Constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations have been done in the world of Constraint Optimization Problems (COP). In this work, we provide a generalization to COP as well as an empirical evaluation of different state of the art existing CSP portfolio approaches properly adapted to deal with COP. The results obtained by measuring several evaluation metrics confirm the effectiveness of portfolios even in the optimization field, and could give rise to some interesting future research.

Original languageEnglish
JournalAnnals of Mathematics and Artificial Intelligence
Volume76
Issue number1-2
Pages (from-to)229-246
Number of pages18
ISSN1012-2443
DOIs
Publication statusPublished - 1. Feb 2016
Externally publishedYes

Keywords

  • Algorithm portfolio
  • Artificial intelligence
  • Combinatorial optimization
  • Constraint programming
  • Machine learning

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