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 language | English |
---|---|
Journal | Annals of Mathematics and Artificial Intelligence |
Volume | 76 |
Issue number | 1-2 |
Pages (from-to) | 229-246 |
Number of pages | 18 |
ISSN | 1012-2443 |
DOIs | |
Publication status | Published - 1. Feb 2016 |
Externally published | Yes |
Keywords
- Algorithm portfolio
- Artificial intelligence
- Combinatorial optimization
- Constraint programming
- Machine learning