Titlebook: Artificial Immune Systems; 11th International C Carlos A. Coello Coello,Julie Greensmith,Mario Pav Conference proceedings 2012 Springer-Ver

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B-cell

0302-9743 disciplines of immunology, biology, medical science, computer science, physics, mathematics and engineering. The scope of AIS ranges from modelling and simulation of the immune system through to immune-inspired algorithms and in silico, in vitro and in vivo solutions.978-3-642-33756-7978-3-642-33757-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Clustering-Based Multi-objective Immune Optimization Evolutionary Algorithms. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms.

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RC-DCA: A New Feature Selection and Signal Categorization Technique for the Dendritic Cell Algorithmbased on Rough Set Theory (RST). In this model, the selection and the categorization processes are based on the RST CORE and REDUCT concepts. Results show that applying RST, instead of PCA, to DCA is more convenient for data pre-processing yielding much better performance in terms of accuracy.

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CSA/IE: Novel Clonal Selection Algorithm with Information Exchange for High Dimensional Global Optimrion proposed. Finally, the proposed CSA/IE is generalized to optimize some hyper-high dimensional (such as 100~1000 dimensions) unimodal and multimodal test functions, and the results show that the proposed algorithm performs well in terms of the stability and the solution quality.

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,Gütekriterien experimenteller Forschung, research field. The second contribution is the introduction of a quick guide containing the main steps for modelling and simulation in immunology, together with challenges that occur during the model development. Further, this paper introduces an example of a simulation problem, where we test our guidelines.

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rove that the B-cell algorithm outperforms these evolutionary algorithms by far. The advantage stems from the use of contiguous hypermutations. The result is another demonstration that relatively simple artificial immune systems can excel over more complex evolutionary algorithms in the domain of optimisation.