Heuristic sampling: a method for predicting the performance of tree searching programs
SIAM Journal on Computing
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Radio Link Frequency Assignment
Constraints
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Approximate resolution of hard numbering problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
SALSA: A Language for Search Algorithms
Constraints
Early Estimates of the Size of Branch-and-Bound Trees
INFORMS Journal on Computing
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Instance-Based Selection of Policies for SAT Solvers
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Bid evaluation in combinatorial auctions: optimization and learning
Software—Practice & Experience
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
ISAC --Instance-Specific Algorithm Configuration
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
An evaluation of machine learning in algorithm selection for search problems
AI Communications - The Symposium on Combinatorial Search
Predicting the size of IDA*'s search tree
Artificial Intelligence
Towards making autotuning mainstream
International Journal of High Performance Computing Applications
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
| Hi-index | 0.00 |
We propose a method called Selection by Performance Prediction (SPP) which allows one, when faced with a particular problem instance, to select a Branch and Bound algorithm from among several promising ones. This method is based on Knuth's sampling method which estimates the efficiency of a backtrack program on a particular instance by iteratively generating random paths in the search tree. We present a simple adaptation of this estimator in the field of combinatorial optimization problems, more precisely for an extension of the maximal constraint satisfaction framework. Experiments both on random and strongly structured instances show that, in most cases, the proposed method is able to select, from a candidate list, the best algorithm for solving a given instance.