Artificial Immune System Programming for Symbolic Regression

  • Colin G. Johnson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)


Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism. This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming. Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in program induction.


Genetic Programming Clonal Selection Complex Adaptive System Symbolic Regression Immune Memory 
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 2003

Authors and Affiliations

  • Colin G. Johnson
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterbury, KentEngland

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