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On Diversity and Artificial Immune Systems: Incorporating a Diversity Operator into aiNet

  • Paul S. Andrews
  • Jon Timmis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)

Abstract

When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.

Keywords

Data Cluster Algorithm Version Immune Network Multimodal Function Receptor Editing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul S. Andrews
    • 1
  • Jon Timmis
    • 2
  1. 1.Department of Computer ScienceUniversity of YorkUK
  2. 2.Departments of Electronics and Computer ScienceUniversity of YorkUK

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