<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Real-Valued GCS Classifier System

Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Real-Valued GCS Classifier System"}

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not...

Real-Valued GCS Classifier System

Publié en ligne: 07 janv. 2008

Pages: 539 - 547

DOI: https://doi.org/10.2478/v10006-007-0044-x

Mots clés
learning classifier systems, GCS, GAs, grammatical inference, context-free grammar

This content is open access.

Real-Valued GCS Classifier System

Publié en ligne: 07 janv. 2008

Pages: 539 - 547

DOI: https://doi.org/10.2478/v10006-007-0044-x

Mots cléslearning classifier systems, GCS, GAs, grammatical inference, context-free grammar

This content is open access.

Mots clés
learning classifier systems, GCS, GAs, grammatical inference, context-free grammar