A Preliminary Survey on Artificial Immune Systems (AIS): A Review on Their Techniques, Strengths and Drawbacks

Abstract

An Artificial Immune System (AIS) is defined as computational intelligence evolved from immunology (biological immune systems) that tries to replicate the way human defensive system works. In this paper, the researchers categorize, compare, and summarize all the major techniques and algorithms of AIS. Particularly, the discussion highlights on the theoretical aspects, techniques, and algorithms of three well-established techniques of AIS, namely the negative selection algorithm, immune network algorithms, and clonal selection algorithms by focusing on their similarities and differences. In addition, this paper elaborates the differences among the techniques and algorithms in terms of the types of data, learning, concepts, elements, generation and operations, strengths and drawbacks, application areas and the evolution of AIS techniques. The techniques selected for this research was guided by beneficial previous literatures carried out by Dasgupta, de Castro, Timmis, and Luo. The findings of this study can help provide greater insights into the understanding of the three AIS techniques, which can further improve the current practice of practitioners and enrich the body of knowledge, benefiting researchers, educators, students, and others.