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.
A Preliminary Survey on Artificial Immune Systems (AIS): A Review on Their Techniques, Strengths and Drawbacks
Roznim Mohamad Rasli¹, Nor Azah Abdul Aziz¹, Fadhlina Mohd Razali¹, Norita Md Norwawi², Nurlida Basir²
¹Sultan Idris Education University, Tanjung Malim, Malaysia, ²Islamic Science University of Malaysia, Bandar Baru Nilai, Malaysia
Email: roznim@fskik.upsi.edu.my, azah@fskik.upsi.edu.my, fadhlina@fskik.upsi.edu.my, norita@usim.edu.my, nurlida@usim.edu.my
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.
Keywords: Artificial Immune Systems, Artificial Immune Systems Techniques, Negative Selection Algorithms, Immune Network Algorithms, Clonal Selection Algorithms
Introduction
Artificial Immune Systems (AISs) are new immunology systems which are based on computational intelligence, that has helped developed many problem-solving techniques. In fact, some scholars assert that AIS is based on heuristic decision making that has evolved from multi-disciplinary fields, namely immunology, computer science, and engineering, which have been successfully implemented in several industrial applications such as prediction, optimization, computer science, engineering, and mathematics in the last decade of their existence. According to de Castro et al. (2002) Castro et al. (2003), AIS is specifically defined as:
“an adaptive system inspired by theoretical immunology and observed immune functions, principles, models, and mechanisms, which are implemented to problem solving.”
Technically, AIS refer to an intelligent computational tool which includes information processing, self-adapting, and self-learning system (Prashant & Mamta, 2015), that can mimic the mechanisms or processes of the human defensive system in building a shield against foreign encroachers or pathogenic infectious agents (e.g., viruses, bacteria, and other parasites), which are collectively known as pathogens or antigens.
The process of recognizing and categorizing foreign or unknown cells entering the body (non-self-cells or antigens) with the body cells (self-cells) is performed by the unique pattern-recognition capability of AIS (Al-Enezi et al., 2010). Such identification and classification is carried out by the intelligence-distributed task force that uses a network of chemical messengers for communications, working from both local and global viewpoints (Burke & Kendall, 2010). The features of AIS include learning and optimization capability, highly distributed learning and memory, robust self-organization, feature extraction, adaptation, recognition, scalability, and decentralized control mechanism (Ulutas & Konak, 2011; Prashant & Mamta, 2015).
As illustrated in Figure 1, biological immune systems are made of a multi-layered system consisting of p
In-Text Citation: (Rasli et al., 2019)
To Cite this Article: Rasli, R. M., Aziz, N. A. A., Razali, F. M., Norwawi, N. M., & Basir, N. (2019). A Preliminary Survey on Artificial Immune Systems (AIS): A Review on Their Techniques, Strengths and Drawbacks. International Journal of Academic Research in Business and Social Sciences, 9(14), 121–144.
Copyright: © 2019 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
