Future Generation Computer Systems
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
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
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Self-adaptive ant colony optimisation applied to function allocation in vehicle networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Optimisation of autonomous ship manoeuvres applying Ant Colony Optimisation metaheuristic
Expert Systems with Applications: An International Journal
Meta-optimization for parameter tuning with a flexible computing budget
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The consultation timetabling problem at Danish high schools
Journal of Heuristics
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Stochastic local search algorithms require finding an appropriate setting of their parameters in order to reach high performance. The parameter tuning approaches that have been proposed in the literature for this task can be classified into two families: on-line and off-line tuning. In this paper, we compare the results we achieved with these two approaches. In particular, we report the results of an experimental study based on a prominent ant colony optimization algorithm, MAX-MIN Ant System, for the traveling salesman problem. We observe the performance of on-line parameter tuning for different parameter adaptation schemes and for different numbers of parameters to be tuned. Our results indicate that, under the experimental conditions chosen here, off-line tuned parameter settings are preferable.