Reducing calibration effort for clonal selection based algorithms: A reinforcement learning approach

Maria Cristina Riff 1 Elizabeth Montero 1 Bertrand Neveu 2, 3, 4
4 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : In this paper we introduce (C, n)-strategy which improves the former C-strategy for on-line calibration of Clonal Selection based algorithms. In this approach, we are focused on a trade-off between the intensification and the diversification of the algorithm search. By using our approach, it allows us to reduce the number of the parameters of the algorithm respecting both the original design of the algorithm and its performance. The number of selected cells and the number of clones are dynamically controlled on-line, according to the algorithm's behavior. We report statistical comparisons using well-known clonalg based algorithms for solving combinatorial optimization problems. From the tests, we conclude that the tuning effort for Clonalg based algorithms is strongly reduced using our technique. Moreover, the dynamic control does not decrease the performance of the original version of the algorithm. On the contrary, it has shown to improve it.
Type de document :
Article dans une revue
Knowledge-Based Systems, Elsevier, 2013, 41, pp.54-67
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https://hal-enpc.archives-ouvertes.fr/hal-00806014
Contributeur : Bertrand Neveu <>
Soumis le : vendredi 29 mars 2013 - 12:38:58
Dernière modification le : jeudi 5 juillet 2018 - 14:29:03

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  • HAL Id : hal-00806014, version 1

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Maria Cristina Riff, Elizabeth Montero, Bertrand Neveu. Reducing calibration effort for clonal selection based algorithms: A reinforcement learning approach. Knowledge-Based Systems, Elsevier, 2013, 41, pp.54-67. 〈hal-00806014〉

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