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.

Both the nervous system and the immune system are complex biological systems. Recognition and categorization are the major functions of both systems. The information processing principles of these natural systems inspired in developing intelligent problemsolving techniques, namely, the Artificial Neural Network (ANN) and the Artificial Immune System (AIS). Though ANNs are well established techniques and are widely used, AISs have received very little attention and there have been relatively few applications of the AIS. This paper briefly describes some of the similarities and differences of these two systems from a computational viewpoint. The paper also reports some preliminary comparative results of the artificial systems. 1 Introduction Both neural networks and immunity-based systems are biologically inspired techniques that have the capability of identifying patterns of interest. They use learning, memory, and associative retrieval to solve recognition and classification tasks. ...

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.

We first compare the paradigm of the immunity-based system with other biological paradigm such as neural networks and genetic algorithm. We review studies of the immunity-based system dividing them into three classes: recognition/learning based on Jerne's network, adaptation based on Edelman's selection principle, and search/optimization based on Holland's genetic algorithm. We also propose that the self-identification process is yet another important aspect of the immune system, which has not been much studied. Some implications to computer networks will be discussed.