TY - JOUR
T1 - Portfolio approaches for constraint optimization problems
AU - Amadini, Roberto
AU - Gabbrielli, Maurizio
AU - Mauro, Jacopo
PY - 2016/2/1
Y1 - 2016/2/1
N2 - 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.
AB - 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.
KW - Algorithm portfolio
KW - Artificial intelligence
KW - Combinatorial optimization
KW - Constraint programming
KW - Machine learning
U2 - 10.1007/s10472-015-9459-5
DO - 10.1007/s10472-015-9459-5
M3 - Journal article
AN - SCOPUS:84929688621
SN - 1012-2443
VL - 76
SP - 229
EP - 246
JO - Annals of Mathematics and Artificial Intelligence
JF - Annals of Mathematics and Artificial Intelligence
IS - 1-2
ER -