The receptor density algorithm

Nick D.L. Owens*, Andrew Greensted, Jon Timmis, Andy Tyrrell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (SciVal)


This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm's performance on two types of anomaly detection problem.

Original languageEnglish
Pages (from-to)51-73
Number of pages23
JournalTheoretical Computer Science
Publication statusPublished - 15 Apr 2013


  • Anomaly detection
  • Artificial immune systems
  • Bio-inspired algorithms
  • Exponential smoothing
  • Kernel density estimation
  • Recurrent equations
  • T cell signalling
  • Weighted moving average


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