Abstract
In this paper, we focus on the potential for applying Kernel Methods into Artificial Immune Systems. This is based on the fact that the commonly employed “affinity functions” can usually be replaced by kernel functions, leading to algorithms operating in the feature space. A discussion of this applicability in negative/positive selection algorithms, the dendritic cell algorithm and immune network algorithms is conducted. As a practical application, we modify the aiNet (Artificial Immune Network) algorithm to use a kernel function, and analyze its compression quality using synthetic datasets. It is concluded that the use of properly adjusted kernel functions can improve the compression quality of the algorithm. Furthermore, we briefly discuss some of the future implications of using kernel functions in immune-inspired algorithms.
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Guzella, T.S., Mota-Santos, T.A., Caminhas, W.M. (2008). Artificial Immune Systems and Kernel Methods. In: Bentley, P.J., Lee, D., Jung, S. (eds) Artificial Immune Systems. ICARIS 2008. Lecture Notes in Computer Science, vol 5132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85072-4_27
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DOI: https://doi.org/10.1007/978-3-540-85072-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85071-7
Online ISBN: 978-3-540-85072-4
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