TY - GEN
T1 - Autonomous tuning for constraint programming via artificial bee colony optimization
AU - Soto, Ricardo
AU - Crawford, Broderick
AU - Mella, Felipe
AU - Flores, Javier
AU - Galleguillos, Cristian
AU - Misra, Sanjay
AU - Johnson, Franklin
AU - Paredes, Fernando
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Constraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.
AB - Constraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.
KW - Adaptive systems
KW - Artificial intelligence
KW - Metaheuristics
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=84948979394&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-21404-7_12
DO - 10.1007/978-3-319-21404-7_12
M3 - Conference contribution
AN - SCOPUS:84948979394
SN - 9783319214030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 171
BT - Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings
A2 - Misra, Sanjay
A2 - Apduhan, Bernady O.
A2 - Murgante, Beniamino
A2 - Gavrilova, Marina L.
A2 - Taniar, David
A2 - Gervasi, Osvaldo
A2 - Misra, Sanjay
A2 - Torre, Carmelo
A2 - Rocha, Ana Maria A.C.
PB - Springer Verlag
T2 - 15th International Conference on Computational Science and Its Applications, ICCSA 2015
Y2 - 22 June 2015 through 25 June 2015
ER -