Learning using an artificial immune system
Learning using an artificial immune system
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DOI：
10.1006/jnca.1996.0014
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发表时间：
1996-04
影响因子：
2.7
通讯作者：
J. Hunt;Denise E. Cooke
中科院分区：
文献类型：
--
作者：
J. Hunt;Denise E. Cooke
摘要在本文中，我们描述了一个人工免疫系统（AIS），它是基于自然免疫系统的模型。这个自然系统是进化学习机制的一个例子，它拥有内容可寻址记忆和“忘记”很少使用的信息的能力。它也是自适应非线性网络的一个例子，其中控制是分散的，问题处理是有效的。因此，免疫系统有可能提供新的解决问题的方法。AIS是围绕当前对免疫系统的理解而开发的系统的一个例子。它说明了人工免疫系统如何捕获免疫系统的基本元素并展示其一些主要特征。我们说明了一个简单的模式识别问题的潜力的AIS。然后，我们将AIS应用于现实世界的问题：DNA序列中启动子的识别。所得结果与神经网络和Quinlan的ID3等方法一致，优于最近邻算法。AIS的主要优点是它只需要正面的例子，并且可以明确地检查它所学习的模式。此外，由于它是自组织的，因此不需要努力优化任何系统参数。
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.