The Negative selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the last decade and categorizes those based on different characteristics and performances. Our study shows that NSA's evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSA's development and highlight challenges in NSA research in comparison with other similar models.

翻译：负选择算法(NSA)是免疫计算(人工免疫系统)领域的重要方法之一。多年来,已经取得一些进展,将这种算法(NSA)转化为解决不同领域问题的有效方法。本审查考虑到过去十年中这些进步迹象,并根据不同特点和表现进行分类。我们的研究显示,消极安全保证的演变可以用四种方式加以标记,突出最显著的消极安全保证差异及其在不同应用领域的局限性。我们还提出了消极安全保证的替代方法,以供比较和分析。很明显,消极安全保证在非线性代表性方面比大多数其他方法表现得更好,在计算时可以超越基于神经的模型。我们总结了消极安全保证的发展情况,并着重指出了与其他类似模型相比,消极安全保证研究中的挑战。