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

María Cristina Riff, Elizabeth Montero, Bertrand Neveu

Resultado de la investigación: Contribución a una revistaArtículo

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)54-67
Número de páginas14
PublicaciónKnowledge-Based Systems
Volumen41
DOI
EstadoPublicada - 1 mar 2013

Áreas temáticas de ASJC Scopus

  • Software
  • Sistemas de gestión de la información
  • Gestión y sistemas de información
  • Inteligencia artificial

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