Revisiting Negative Selection Algorithms
Revisiting Negative Selection Algorithms
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DOI：
10.1162/evco.2007.15.2.223
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发表时间：
2007-06
影响因子：
6.8
通讯作者：
Zhou Ji;D. Dasgupta
中科院分区：
文献类型：
--
作者：
Zhou Ji;D. Dasgupta
本文综述了人工免疫系统（AIS）中的一种异常/变化检测方法--阴性选择算法的研究进展。根据其初始模型，我们试图确定这一系列算法的基本特征，并总结其特点。在该方法中存在各种元素，包括数据表示，覆盖率估计，亲和度度量，和匹配规则，这是讨论了不同的变化。各种否定选择算法也按不同的标准进行分类。与其他AIS或其他机器学习方法的关系和可能的组合进行了讨论。在此基础上，展望了否定选择算法的发展前景和适用性，以及对相关领域的影响。
This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion.