Artificial immune systems
Aickelin, Uwe; Dasgupta, D
Edmund K. Burke
The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years.
A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.
Aickelin, U., & Dasgupta, D. (2005). Artificial immune systems. In E. K. Burke, & G. Kendall (Eds.), Search Methodologies : Introductory Tutorials in Optimisation, Decision Support Techniques. Springer. https://doi.org/10.1007/0-387-28356-0
|Publication Date||Jan 1, 2005|
|Deposit Date||Oct 12, 2007|
|Publicly Available Date||Oct 12, 2007|
|Peer Reviewed||Peer Reviewed|
|Book Title||Search Methodologies : Introductory Tutorials in Optimisation, Decision Support Techniques|
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