Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
Classifier fitness based on accuracy
Evolutionary Computation
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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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.