C-Strategy: A dynamic adaptive strategy for the CLONALG algorithm

María Cristina Riff, Elizabeth Montero, Bertrand Neveu

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

3 Citas (Scopus)

Resumen

The control of parameters during the execution of bio-inspired algorithms is an open research area. In this paper, we propose a new parameter control strategy for the immune algorithm CLONALG. Our approach is based on reinforcement learning ideas. We focus our attention on controlling the number of clones. Our approach provides an efficient and low cost adaptive technique for parameter control. We use instances of the Travelling Salesman Problem. The results obtained are very encouraging.

Idioma originalInglés
Título de la publicación alojadaTransactions on Computational Science VIII
EditoresMarina L. Gavrilova, Chih Jeng Kenneth Tan
Páginas41-55
Número de páginas15
DOI
EstadoPublicada - 9 nov 2010

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen6260 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'C-Strategy: A dynamic adaptive strategy for the CLONALG algorithm'. En conjunto forman una huella única.

  • Citar esto

    Riff, M. C., Montero, E., & Neveu, B. (2010). C-Strategy: A dynamic adaptive strategy for the CLONALG algorithm. En M. L. Gavrilova, & C. J. K. Tan (Eds.), Transactions on Computational Science VIII (pp. 41-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6260 LNCS). https://doi.org/10.1007/978-3-642-16236-7_3