  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.
