C-Strategy

A dynamic adaptive strategy for the CLONALG algorithm

María Cristina Riff, Elizabeth Montero, Bertrand Neveu

Resultado de la investigación: Conference contribution

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 originalEnglish
Título de la publicación alojadaTransactions on Computational Science VIII
EditoresMarina L. Gavrilova, Chih Jeng Kenneth Tan
Páginas41-55
Número de páginas15
DOI
EstadoPublished - 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

Huella dactilar

Adaptive Strategies
Adaptive Techniques
Immune Algorithm
Travelling salesman problems
Reinforcement Learning
Clone
Control Parameter
Control Strategy
Traveling salesman problem
Reinforcement learning
Strategy
Costs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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
Riff, María Cristina ; Montero, Elizabeth ; Neveu, Bertrand. / C-Strategy : A dynamic adaptive strategy for the CLONALG algorithm. Transactions on Computational Science VIII. editor / Marina L. Gavrilova ; Chih Jeng Kenneth Tan. 2010. pp. 41-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "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.",
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Riff, MC, Montero, E & Neveu, B 2010, C-Strategy: A dynamic adaptive strategy for the CLONALG algorithm. En ML Gavrilova & CJK Tan (eds.), Transactions on Computational Science VIII. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6260 LNCS, pp. 41-55. https://doi.org/10.1007/978-3-642-16236-7_3

C-Strategy : A dynamic adaptive strategy for the CLONALG algorithm. / Riff, María Cristina; Montero, Elizabeth; Neveu, Bertrand.

Transactions on Computational Science VIII. ed. / Marina L. Gavrilova; Chih Jeng Kenneth Tan. 2010. p. 41-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6260 LNCS).

Resultado de la investigación: Conference contribution

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AU - Riff, María Cristina

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AU - Neveu, Bertrand

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Riff MC, Montero E, Neveu B. C-Strategy: A dynamic adaptive strategy for the CLONALG algorithm. En Gavrilova ML, Tan CJK, editores, Transactions on Computational Science VIII. 2010. p. 41-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-16236-7_3