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. © 2010 Springer-Verlag.
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CITATION STYLE
Riff, M. C., Montero, E., & Neveu, B. (2010). C-Strategy: A dynamic adaptive strategy for the CLONALG algorithm. In 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
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