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The Role of Early Stopping and Population Size in XCS for Intrusion Detection

  • Kamran Shafi
  • Hussein A. Abbass
  • Weiping Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

Evolutionary Learning Classifier Systems (LCSs) are rule based systems that have been used effectively in concept learning. XCS is a prominent LCS that uses genetic algorithms and reinforcement learning techniques. In traditional machine learning (ML), early stopping has been investigated extensively to the extent that it is now a default mechanism in many systems. However, there has been a belief that EC methods are more resilient to overfitting. Therefore, this topic is under-investigated in the evolutionary computation literature and has not been investigated in LCS. In this paper, we show that it is necessary to stop evolution in LCS using a stopping criteria other than a maximum number of generations and that evolution may suffer from overfitting similar to other ML methods.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kamran Shafi
    • 1
  • Hussein A. Abbass
    • 1
  • Weiping Zhu
    • 1
  1. 1.Defence and Security Applications Research CentreUniv. of New South Wales @ ADFACanberraAustralia

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