Future Generation Computer Systems
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization
SIAM Journal on Optimization
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Applications of racing algorithms: an industrial perspective
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Estimation-based metaheuristics for the probabilistic traveling salesman problem
Computers and Operations Research
Off-line vs. on-line tuning: a study on MAX–MIN ant system for the TSP
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Adaptive "Anytime" two-phase local search
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
An incremental ant colony algorithm with local search for continuous optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and Artificial Intelligence
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Macro learning in planning as parameter configuration
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Automatic generation of multi-objective ACO algorithms for the bi-objective knapsack
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
On the anytime behavior of IPOP-CMA-ES
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Ordered racing protocols for automatically configuring algorithms for scaling performance
Proceedings of the 15th annual conference on Genetic and evolutionary computation
An analysis of post-selection in automatic configuration
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
The consultation timetabling problem at Danish high schools
Journal of Heuristics
Region based memetic algorithm for real-parameter optimisation
Information Sciences: an International Journal
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Finding appropriate values for the parameters of an algorithm is a challenging, important, and time consuming task. While typically parameters are tuned by hand, recent studies have shown that automatic tuning procedures can effectively handle this task and often find better parameter settings. F-Race has been proposed specifically for this purpose and it has proven to be very effective in a number of cases. F-Race is a racing algorithm that starts by considering a number of candidate parameter settings and eliminates inferior ones as soon as enough statistical evidence arises against them. In this paper, we propose two modifications to the usual way of applying F-Race that on the one hand, make it suitable for tuning tasks with a very large number of initial candidate parameter settings and, on the other hand, allow a significant reduction of the number of function evaluations without any major loss in solution quality. We evaluate the proposed modifications on a number of stochastic local search algorithms and we show their effectiveness.