Artificial Immune Systems

By Jonathan Timmis. Lecture Notes in Computer Science, 2009

781 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/d36ea7e72d0f2aed12b9e362ec4ba6b7f22eb9ff

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In Biomedical Engineering

Citations in the corpus

Application Areas of AIS: The Past, The Present and The Future (2005, background): “However, in order to overcome some of these shortfalls, work proposed in [5] and later expanded on in [ 6 ] proposes the adoption of the danger theory approach.”
Advances in artificial immune systems (2006, positive): “The T-cell algorithm worked in unison with the novel danger-theory inspired system [ 62 ].”
When immediate interactive feedback boosts optimization problem solving: A 'human-in-the-loop' approach for solving Capacitated Vehicle Routing Problems (2017, methodology): “…as exact algorithms, heuristics and metaheuristics that address large instances (e.g. high number of customers to deliver items to) are currently being employed for solving such problems (Blasum & Hochstaettler, 2000; Fisher, 1994; Lee & Mitchell, 1998; Toth & Vigo, 2001; Burke & Kendall, 2005).”
Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM (2017, methodology): “Many methods have been proposed such as: Random forest [4] [5], Decision Tree [6], K-Nearest Neighbors [7], SVM [8] [9], HMM [10], neural networks [11], Bayesian learning [11], artificial immune systems [12], association rules [13], hybrid models [14], discriminant analysis [15].”