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Real-valued GCS classifier system

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Cielecki Ł., Unold O.

International Journal of Applied Mathematics and Computer Science

2007

tom 17

nr 4

539-547

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 real-valued 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.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Cielecki Ł.

1 Unold O.

1 2007

Wyniki wyszukiwania

Real-valued GCS classifier system