Theoretical advances in artificial immune systems

Timmis, Jon; Hone, Andrew N. W.; Stibor, Thomas; Clark, Edward

doi:10.1016/j.tcs.2008.02.011

Cited by 265 publications

(161 citation statements)

References 31 publications

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“…It is important to put our work in context with the work carried out by Stibor [3,6,9], who demonstrated that the problem of deciding if any r-contiguous detector is generable for a given self-set can be expressed as an instance of the k-CNF satisfiability problem (SAT), which is N P-complete. This result is not a contradiction to our findings.…”

Section: Discussionmentioning

confidence: 99%

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Efficient Algorithms for String-Based Negative Selection

Elberfeld

Textor

2009

Lecture Notes in Computer Science

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Abstract. String-based negative selection is an immune-inspired classification scheme: Given a self-set S of strings, generate a set D of detectors that do not match any element of S. Then, use these detectors to partition a monitor set M into self and non-self elements. Implementations of this scheme are often impractical because they need exponential time in the size of S to construct D. Here, we consider r-chunk and r-contiguous detectors, two common implementations that suffer from this problem, and show that compressed representations of D are constructible in polynomial time for any given S and r. Since these representations can themselves be used to classify the elements in M , the worst-case running time of r-chunk and r-contiguous detector based negative selection is reduced from exponential to polynomial.

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Section: Discussionmentioning

confidence: 99%

“…In principle, the infeasibility of generating all detectors might be due to some intrinsic hardness of the problem -maybe, as argued by Timmis et al [6], it could be equivalent to an N P-hard problem. However, we show in this paper that this is not the case.…”

Section: Definition 23 (Detector Sets) Given a Self-set S ⊆ σmentioning

confidence: 99%

Efficient Algorithms for String-Based Negative Selection

Elberfeld

Textor

2009

Lecture Notes in Computer Science

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“…There are many astonishing attempts to analyze and optimize complex systems, and proclaim hidden facets in literature. The most famous bio-inspired algorithms include genetic algorithm [1] , neural network [14] , immune system [24] . Multi-agent system simulates society and human decision behavior [16,20] .…”

Section: Bio-nspired Approachesmentioning

confidence: 99%

“…In Eq. (23) (22) n N Aff APPEND (23) n N Aff REMOVE (24) (6) Proliferation Proliferation is an interesting mechanism to reproduce productivity when the demand is increased for a lot. It is a general principle in immune system and ecosystem when the invasion happens.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

A multi-affinity model for logistics network inspired by bio-system

Hu¹,

Sheng²

2014

IJCIS

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How can we deal with the increasing scale of large logistics network for the booming economy? The structure, mechanisms and principles upon the logistics network show an evolutionary tendency which is complex, and interesting for designing and optimizing logistics network. The adaptability, stability and evolution capability are attractive for artificial intelligent systems. Inspired by biological systems, the metaphors for the structure and eight primary mechanisms are extracted to design a multi-affinity model for bio-inspired logistics network. Then, by mapping the biological elements and functions to logistics network, the evolutionary structure and simulation approach for logistics network are proposed and investigated by experiments. It is a novel way to study the complexity of logistics network and it is an interesting bionic approach for artificial network model. The research result can be employed to study the emergency features and collective behavior of large-scale logistics network.

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“…In AIS, one popular anomaly detection algorithm is known as the negative selection algorithm, which was shown to have an exponential runtime complexity [30]. An attempt of reducing the worstcase runtime complexity from exponential to polynomial was reported in [9], however this reduction is only applicable when the input feature space is bit strings instead of real numbers.…”

Section: The Standard Dcamentioning

confidence: 99%

Theoretical Formulation and Analysis of the Deterministic Dendritic Cell Algorithm

Greensmith

Aickelin

2013

SSRN Journal

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As one of the emerging algorithms in the field of Artificial 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 definition, 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 definition of the algorithm, as well as to perform runtime analyses to reveal its theoretical aspects. In this paper, we define 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(n 2 ) 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.

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Theoretical advances in artificial immune systems

Cited by 265 publications

References 31 publications

Efficient Algorithms for String-Based Negative Selection

Efficient Algorithms for String-Based Negative Selection

A multi-affinity model for logistics network inspired by bio-system

Theoretical Formulation and Analysis of the Deterministic Dendritic Cell Algorithm

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