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摘要翻译：
人类免疫系统具有鲁棒性和容错性等诸多优点，在计算领域具有广阔的应用前景，许多不同的算法被称为人工免疫系统(AIS)。现时使用两代自动识别系统，第一代依赖简化的免疫模型，第二代则利用跨学科合作，加深对免疫系统的理解，从而产生更复杂的模型。这两代算法已经成功地应用于各种问题，包括异常检测、模式识别、优化和机器人技术。在这一章中，我们介绍了AIS的概况，讨论了它的演化，并表明该领域的多样性与免疫系统本身的多样性有关，导致了许多算法而不是一个原型系统。两个案例研究也被提出，以帮助提供对AIS机制的洞察力；这两种方法分别是独特型网络法和树突状细胞法。
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英文标题：
《Artificial Immune Systems (2010)》
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作者：
Julie Greensmith, Amanda Whitbrook, Uwe Aickelin
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.
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PDF链接：
https://arxiv.org/pdf/1006.4949
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