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.
@article{bwmeta1.element.bwnjournal-article-amcv17i4p539bwm,
author = {Cielecki, \L ukasz and Unold, Olgierd},
title = {Real-valued GCS classifier system},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {17},
year = {2007},
pages = {539-547},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv17i4p539bwm}
}
Cielecki, Łukasz; Unold, Olgierd. Real-valued GCS classifier system. International Journal of Applied Mathematics and Computer Science, Tome 17 (2007) pp. 539-547. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv17i4p539bwm/
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