A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
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Currently, there exist several offline calibration techniques that can be used to fine-tune the parameters of a metaheuristic. Such techniques require, however, to perform a considerable number of independent runs of the metaheuristic in order to obtain meaningful information. Here, we are interested on the use of this information for assisting the algorithm designer to discard components of a metaheuristic (e.g., an evolutionary operator) that do not contribute to improving its performance (we call them "ineffective components"). In our study, we experimentally analyze the information obtained from three offline calibration techniques: F-Race, ParamILS and Revac. Our preliminary results indicate that these three calibration techniques provide different types of information, which makes it necessary to conduct a more in-depth analysis of the data obtained, in order to detect the ineffective components that are of our interest.