A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Connections in networks: a hybrid approach
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
A gender-based genetic algorithm for the automatic configuration of algorithms
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
An empirical study of optimization for maximizing diffusion in networks
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
Dynamic scoring functions with variable expressions: new SLS methods for solving SAT
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Ordered racing protocols for automatically configuring algorithms for scaling performance
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Automated algorithm configurators have been shown to be very effective for finding good configurations of high performance algorithms for a broad range of computationally hard problems. As we show in this work, the standard protocol for using these configurators is not always effective. We propose a simple and computationally inexpensive modification to this protocol and apply it to state-of-the-art solvers for two prominent problems, TSP and computer Go playing, where the standard protocol is unable or unlikely to yield performance improvements, and one problem, mixed integer programming, where the standard protocol is known to be effective. We show that our new protocol is able to find configurations between 4% and 180% better than the standard protocol within the same time budget.