Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Approximating Min Sum Set Cover
Algorithmica
Adaptive Routing Using Expert Advice
The Computer Journal
Restart schedules for ensembles of problem instances
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Minimizing regret with label efficient prediction
IEEE Transactions on Information Theory
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
Restart schedules for ensembles of problem instances
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The TPTP Problem Library and Associated Infrastructure
Journal of Automated Reasoning
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A constraint satisfaction framework for executing perceptions and actions in diagrammatic reasoning
Journal of Artificial Intelligence Research
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Algorithm selection and scheduling
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a set of heuristics whose only observable behavior is their running time. Each heuristic can compute a solution to any problem instance, but its running time varies across instances. The user solves each instance by interleaving runs of the heuristics according to a task-switching schedule. We present (i) exact and approximation algorithms for computing an optimal task-switching schedule offline, (ii) sample complexity bounds for learning a task-switching schedule from training data, and (iii) a no-regret strategy for selecting task-switching schedules online. We demonstrate the power of our results using data from recent solver competitions. We outline how to extend our results to the case in which the heuristics are randomized, and the user may periodically restart each heuristic with a fresh random seed.