Abstract
Artificial immune systems can be applied to a variety of very different tasks including classical function optimization. There are even artificial immune systems tailored specifically for this task. In spite of the successful application there is little knowledge and hardly any theoretical investigation about how and why they perform well. Here a rigorous analysis for a specific type of mutation operator introduced for function optimization called somatic contiguous hypermutation is presented. While there are serious limitations to the performance of this operator even for simple optimization tasks it is proven that for some types of optimization problems it performs much better than standard bit mutations most often used in evolutionary algorithms.
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Jansen, T., Zarges, C. (2009). A Theoretical Analysis of Immune Inspired Somatic Contiguous Hypermutations for Function Optimization. In: Andrews, P.S., et al. Artificial Immune Systems. ICARIS 2009. Lecture Notes in Computer Science, vol 5666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03246-2_12
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DOI: https://doi.org/10.1007/978-3-642-03246-2_12
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