An Overview of Artificial Immune Systems

Timmis, Jon; Knight, Thomas Andrew; Castro, Leandro Nunes de; Hart, Emma

doi:10.1007/978-3-662-06369-9_4

Cited by 107 publications

(57 citation statements)

References 59 publications

(85 reference statements)

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“…The immune system is computationally one of the least understood biological paradigms but has drawn significant attention. AIS started to be used in many application domains including computer security, optimization, robotics, data mining, fault detection, anomaly detection, and pattern recognition [12].…”

Section: Immune Techniques For Character Recognitionmentioning

confidence: 99%

Licence Plate Character Recognition Using Artificial Immune Technique

Huang

Tawfik

Nagar

2008

Computational Science – ICCS 2008

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Abstract. This paper proposes the application of Artificial Immune Technique in Licence Plate Character Recognition (LPCR). The use of Clonal Selection Algorithm (CSA) is composed of two main stages: (1) dynamic training samples; and (2) a choice of the best antibodies based on the three main clonal operations of cloning, clonal mutation and clonal selection. Once memory cells are established it will output the classification results using Fuzzy K-Nearest Neighbor (KNN) approach. The performance of CSA is compared to the Back Propagation Neural Networks (BPNN) in solving a LPCR problem. The experimental results show that the Artificial Immune Technique has a favorable performance in terms of being more accurate and robust.

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Section: Immune Techniques For Character Recognitionmentioning

confidence: 99%

Licence Plate Character Recognition Using Artificial Immune Technique

Huang

Tawfik

Nagar

2008

Computational Science – ICCS 2008

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“…The model presented there uses a virtual thymus which causes the AIS to become tolerant to previously unseen normal behavior of the nodes, but automatically learns and detects new misbehavior. Further applications of AIS to computing and networking as well as to other fields can be found in [28].…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Biologically Inspired Networking

2007

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“…Over the last few years, there has been an ever increasing interest in the area of Artificial Immune Systems and their applications in pattern recognition and optimization such as [5], [8], [9], [20], [22], [23], [35], [36] and many more.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System

Tan¹

2015

Anti-Spam Techniques Based on Artificial Immune System

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Artificial Immune System algorithms use antibodies which fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm can not make use of schemata or classes of partial solutions. This paper presents a symbiotic artificial immune system (SymbAIS) algorithm which is an extension of CLONALG algorithm. It uses partially specified antibodies and gradually builds up building blocks of suitable sub-antibodies. The algorithm is compared with CLONALG on multimodal function optimization and combinatorial optimization problems and it is shown that it can solve problems that CLONALG is unable to solve.

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An Overview of Artificial Immune Systems

Abstract: The immune system is highly distributed, highly adaptive, self-organising

Cited by 107 publications

References 59 publications

Licence Plate Character Recognition Using Artificial Immune Technique

Licence Plate Character Recognition Using Artificial Immune Technique

Biologically Inspired Networking

Artificial Immune System

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