摘要翻译：
在过去的几年里，越来越多的启发式决策技术受到自然界的启发，如进化算法、蚁群优化和模拟退火。近年来，一种受免疫学启发的新型计算智能技术出现了，即人工免疫系统(AIS)。这种受免疫系统启发的技术已经在解决一些计算问题中发挥了作用。在这个主题中，我们将非常简要地描述与AIS相关的免疫系统隐喻。然后，我们将给出一些适合AIS使用的示例性实际问题，并展示一步一步的算法演练。AIS与其他著名算法的比较和未来工作的领域将结束这个主题。应该注意的是，由于AIS仍然是一个年轻和不断发展的领域，还没有一个固定的算法模板，因此实际的实现可能与这里给出的示例有所不同。
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英文标题：
《Artificial Immune Systems (AIS) - A New Paradigm for Heuristic Decision
  Making》
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作者：
Uwe Aickelin
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最新提交年份：
2008
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分类信息：

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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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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英文摘要：
  Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a step-by-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here.
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PDF链接：
https://arxiv.org/pdf/0801.4314