The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness ’ and ‘effectiveness. ’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory. ’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

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this report (De Castro & Von Zuben, 1999) is intended to present the basic theory and concepts necessary for the development of immune-based systems. It brings an instructive introduction to the mammal immune system and depicts its most relevant aspects from the viewpoint of engineering. Mechanisms like the clonal selection theory, the immune response along with its affinity maturation process and the immune network hypothesis are emphasized. A few computational algorithms were developed and applied to several different types of problems in order to demonstrate how principles gleaned from the immune system can and must be used in the design of engineering tools for solving complex tasks. In addition, it is introduced an emerging area of research, called immune engineering. The immune engineering is comprised of several strategies, like artificial immune systems, immune-based systems, immunogenetic approaches, etc., and is supposed to include any technique developed using ideas from immunology.

Artificial Immune System (AIS) is a novel evolutionary paradigm inspired by the biological aspects of the immune system. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering tasks. This paper discusses two concepts. (a) The behavioral management of artificial intelligence (AI) namely the intelligent multi agent systems, (b) The evolutionary computation called the artificial immune system that imitates the biological theory called the immune system. The outcome of this research is an Artificial Immune System based Intelligent Multi Agent Model named AISIMAM that solves agent-based applications. The model is applied to a mine detection and diffusion problem and the results prove that AISIMAM has solved the problem successfully.