A beginner's guide to tuning methods

Elizabeth Montero 1 Maria Cristina Riff 1 Bertrand Neveu 2, 3, 4
2 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : Metaheuristic methods have been demonstrated to be efficient tools to solve hard optimization problems. Most metaheuristics define a set of parameters that must be tuned. A good setup of that parameter values can lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategies are step by step methods based on multiple runs of the metaheuristic algorithm. In this study we compare four automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of each method using a standard genetic algorithm for continuous function optimization. We discuss about the requirements of each method, the resources used and quality of solutions found in different scenarios. Finally we establish some guidelines that can help to choose the more appropriate tuning procedure.
Type de document :
Article dans une revue
Applied Soft Computing, Elsevier, 2014, 17, pp.39-51
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https://hal-enpc.archives-ouvertes.fr/hal-01077479
Contributeur : Bertrand Neveu <>
Soumis le : vendredi 24 octobre 2014 - 17:49:51
Dernière modification le : mercredi 4 juillet 2018 - 16:33:26

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  • HAL Id : hal-01077479, version 1

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Elizabeth Montero, Maria Cristina Riff, Bertrand Neveu. A beginner's guide to tuning methods . Applied Soft Computing, Elsevier, 2014, 17, pp.39-51. 〈hal-01077479〉

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