Negative Selection Algorithm Research and Applications in the last decade: A Review

TL;DR

This review summarizes the progress and variations of the Negative Selection Algorithm over the past decade, highlighting its strengths in nonlinear representation and computational efficiency compared to neural models.

Contribution

It categorizes NSA developments over the last decade, compares alternative approaches, and discusses its advantages and limitations across different application domains.

Findings

NSA performs better for nonlinear data representation.

NSA can outperform neural models in computation time.

The review identifies key variations and challenges in NSA research.

Abstract

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.