Immunity-based systems: a survey

Dasgupta, Dipankar; Attoh-Okine, Nii

doi:10.1109/icsmc.1997.625778

Cited by 164 publications

(72 citation statements)

References 18 publications

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“…This is by no means limited to computer models of the immune system which are used by biologists to gain a better understanding of the subject. Detailed reviews of the various AIS architectures which exist can be found in [3] and [4]. This section briefly outlines the operation of the human immune system, before detailing the nature of the negative selection algorithm, which forms the basis of experiments carried out later in the paper.…”

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confidence: 99%

An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems

Taylor

Corne

2003

Lecture Notes in Computer Science

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Abstract. Supermarkets lose millions of pounds every year through lost trading and stock wastage caused by the failure of refrigerated cabinets. Therefore, a huge commercial market exists for artificially intelligent systems which are able to detect the early symptoms of faults. Previous work in this vein, using real-world data and now in the throes of being deployed commercially, has employed evolved neural networks to predict volumes of temperature and other alarms emerging from refrigeration system controllers, and also to predict likely refrigerant gas loss from such alarm patterns. In this work we turn to the use of in-cabinet temperature data which has recently become available, and the aim is to predict refrigeration system faults from the pattern of in-cabinet temperature over time. We argue that artificial immune system inspired technologies are particularly appropriate for this task. The negative selection algorithm is therefore investigated as a tool to detect anomalous patterns in temperature data. Using a simple AIS system in preliminary experiments to assess feasibility, we compare the performance of a simple matching rule based on Euclidean distance and one using traditional r contiguous bits matching. We also investigate a 'differential' encoding scheme which reduces the size of pattern space while retaining what we feel to be the essential elements of patterns in this application. We find that feasibility for AIS in this application is proven, with particularly good selfdetection rates, and a fault-detection rate adequate as a springboard for further development. The best AIS configuration of those examined here seems to be that which uses the novel differential encoding in conjunction with the r-bits matching rule. The differential encoding scheme is simple, yet seems powerfully suited to such time series pattern analysis tasks.

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confidence: 99%

An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems

Taylor

Corne

2003

Lecture Notes in Computer Science

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“…More detailеd surveys of artificial immune systems and their applications can be found in Refs. (Dasgupta & Attoh-Okine, 1997;Garrett, 2005). The description of the dynamics of the antibody selection mechanism and the artificial immune navigator, as they have been presented in Ref.…”

Section: Biological and Artificial Immune Networkmentioning

confidence: 99%

Emotional Intervention on Stigmergy Based Foraging Behaviour of Immune Network Driven Mobile Robots

Tsankova¹

2008

Frontiers in Evolutionary Robotics

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“…The higher the affinity of a B cell to the available antigens, the more likely it will clone. This is called as a Darwinian process of variation and selection, i.e., affinity maturation [6], [7].…”

Section: A Clonal Expansion Process In Natural Immune Systemmentioning

confidence: 99%

“…Like artificial neural networks and genetic algorithm, AIS are highly abstract models of their biological counterparts applied to solve a number of complex problems in different domains. Some work processes of NIS are used as metaphors to develop novel computing models in computational intelligence, such as negative selection, clonal selection, to name a few, to solve many complex problems in science and engineering [6], [7], [13].…”

Section: A Clonal Expansion Process In Natural Immune Systemmentioning

confidence: 99%

Clonal particle swarm optimization and its applications

Tan

Xiao

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Particle swarm optimization (PSO) is a stochastic global optimization algorithm inspired by social behavior of bird flocking in search for food, which is a simple but powerful, and widely used as a problem-solving technique to a variety of complex problems in science and engineering. A novel particle swarm optimization algorithm based on immunityclonal strategies, called as clonal particle swarm optimization (CPSO), is proposed at first in this paper. By cloning the best individual of ten succeeding generations, CPSO has better optimization solving capability and faster convergence performance than the conventional standard particle swarm optimization (SPSO) based on a number of simulations. A detailed description and explanation of the CPSO algorithm are given in the paper. Several experiments on six benchmark test functions are conducted to demonstrate that the proposed CPSO algorithm is able to speedup the evolution process and improve the performance of global optimizer greatly, while avoiding the premature convergence on the multidimensional variable space.

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Immunity-based systems: a survey

Cited by 164 publications

References 18 publications

An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems

An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems

Emotional Intervention on Stigmergy Based Foraging Behaviour of Immune Network Driven Mobile Robots

Clonal particle swarm optimization and its applications

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