Ji Zhou, Dasgupta Dipankar
AutoZone, Inc., Memphis, TN 38103, USA.
Evol Comput. 2007 Summer;15(2):223-51. doi: 10.1162/evco.2007.15.2.223.
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.
本文回顾了阴性选择算法的进展，这是人工免疫系统（AIS）中的一种异常/变化检测方法。继其初始模型之后，我们试图识别该算法家族的基本特征并总结其多样性。该方法存在各种要素，包括数据表示、覆盖估计、亲和度度量和匹配规则，针对不同的变体对这些要素进行了讨论。各种阴性选择算法也根据不同标准进行了分类。还讨论了与其他人工免疫系统或其他机器学习方法的关系及可能的组合。基于上述讨论，进而推测了阴性选择算法的未来发展和适用性及其对相关领域的影响。
