Journal ArticleDOI
Learning using an artificial immune system
John Hunt,Denise E. Cooke +1 more
- Vol. 19, Iss: 2, pp 189-212
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This paper illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics, and applies the AIS to a real-world problem: the recognition of promoters in DNA sequences.Abstract:
In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to «forget» little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan's ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not require effort to optimize any system parameters.read more
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Artificial Neural Networks
TL;DR: artificial neural networks, artificial neural networks , مرکز فناوری اطلاعات و اصاع رسانی, کδاوρزی
Journal ArticleDOI
Learning and optimization using the clonal selection principle
L.N. de Castro,F.J. Von Zuben +1 more
TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.
The Clonal Selection Algorithm with Engineering Applications 1
TL;DR: A powerful computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response is proposed and is shown to be an evolutionary strategy capable of solving complex machinelearning tasks, like pattern recognition and multimodal optimization.
Journal ArticleDOI
Artificial immune systems as a novel soft computing paradigm
L.N. de Castro,Jon Timmis +1 more
TL;DR: This paper proposes one such framework for AIS, discusses the suitability of AIS as a novel soft computing paradigm and reviews those works from the literature that integrate AIS with other approaches, focusing ANN, EA and FS.
Journal ArticleDOI
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
TL;DR: A survey of the major works in the AIS field explores up-to-date advances in applied AIS during the last few years and reveals that recent research is centered on four major AIS algorithms: negative selection algorithms; artificial immune networks; clonal selection algorithm; Danger Theory and dendritic cell algorithms.