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This research focus area develops new training methods for artificial immune systems. Genetic algorithms and particle swarm optimization techniques are used to optimize the creation of artificial lymphocytes. Approaches are also investigated to optimize the training process of existing learning algorithms. Network-based immune algorithms to cluster of non-stationary data are developed.


List the current members actively doing research in this focus area. [ Show ]


M Poggiolini

M.Sc Completed in 2009

A Graaff

PhD Started in 2005
M.Sc Completed in 2003


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 Attie Graaff

Portrait photo




 Artificial Immune Systems


 Degree specific information: PhD


 Artificial Immune Network Systems for Clustering Dynamically Changing Data


Not Available

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.


 Degree specific information: M.Sc


 The Artificial Immune System with Evolved Lymphocytes


The natural immune system can be modeled into an artificial immune system that can be used to detect any unwanted patterns in a non-biological environment. One of the main tasks of an immune system is to learn the structure of these unwanted patterns for a faster response to future foreign patterns with the same or similar structure. The artificial immune system (AIS) can therefor be seen as a pattern recognition system. The AIS contains artificial lymphocytes (ALC) that classify any pattern either as part of a predetermined set of patterns or not. It is possible for an ALC to classify more than one pattern and even classify a pattern better than other ALCs. The ALCs that never classify any pattern need to be replaced by newly created or evolved ALCs. It is therefore important to know what ALCs need to be replaced so that ALCs with a better classification are kept. In the natural immune system the lymphocytes have different states: Immature, Mature, Memory or Annihilated. Lymphocytes in the annihilated state needs to be replaced by newly created or evolved lymphocytes. The thesis presents an AIS for detection of unwanted patterns and proposes a threshold function to determine the state of an ALC.

 Supervisor / Co-Supervisor:

 AP Engelbrecht



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