Abstract
The aim of this paper is to investigate the antigen sampling component of the deterministic version of the dendritic cell algorithm (dDCA). To achieve this, a model is presented, and used to produce synthetic data for two temporal correlation problems. The model itself is designed to simulate a system stochastically switching between a normal and an anomalous state over time. By investigating five parameter values for the dDCA’s maximum migration threshold, and benchmarking alongside a minimised version of the dDCA, the effect of sampling using a multi-agent population is explored. Potential sources of error in the dDCA outputs are identified, and related to the duration of the anomalous state in the input data.
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Musselle, C.J. (2010). Insights into the Antigen Sampling Component of the Dendritic Cell Algorithm. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_8
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DOI: https://doi.org/10.1007/978-3-642-14547-6_8
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