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Paper details
Number 4 - December 2007
Volume 17 - 2007
Real-valued GCS classifier system
Łukasz Cielecki, Olgierd Unold
Abstract
Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.
Keywords
earning classifier systems, GCS, GAs, grammatical inference, context-free grammar