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Gu, Feng, Greensmith, Julie and Aickelin, Uwe (2013) Theoretical formulation and analysis of the deterministic dendritic cell algorithm. Biosystems, 111 (2). pp. 127-135. ISSN 0303-2647

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Abstract

As one of the emerging algorithms in the eld of Articial Immune Systems(AIS), the Dendritic Cell Algorithm (DCA) has been successfully applied to a number of challenging real-world problems. However, one criticism is the lack of a formal denition, which could result in ambiguity for understanding the algorithm. Moreover, previous investigations have mainly focused on its empirical aspects. Therefore, it is necessary to provide a formal def-

inition of the algorithm, as well as to perform runtime analyses to reveal its theoretical aspects. In this paper, we dene the deterministic version of the DCA, named the dDCA, using set theory and mathematical functions.

Runtime analyses of the standard algorithm and the one with additional segmentation are performed. Our analysis suggests that the standard dDCA has a runtime complexity of O(n2) for the worst-case scenario, where n is the

number of input data instances. The introduction of segmentation changes the algorithm's worst case runtime complexity to O(max(nN; nz)), for DC population size N with size of each segment z. Finally, two runtime variables

of the algorithm are formulated based on the input data, to understand its runtime behaviour as guidelines for further development.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/1004315
Additional Information:	NOTICE: this is the author’s version of a work that was accepted for publication in Biosystems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Biosystems, 111,2, (2013), 10.1016/j.biosystems.2013.01.001
Schools/Departments:	University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:	https://doi.org/10.1016/j.biosystems.2013.01.001
Depositing User:	Aickelin, Professor Uwe
Date Deposited:	09 Aug 2013 17:29
Last Modified:	04 May 2020 20:20
URI:	https://eprints.nottingham.ac.uk/id/eprint/2071

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