Real-Valued GCS Classifier System

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 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.

• Publication year

  2007

• Venue

  International Journal of Applied Mathematics and Computer Sciences

• Publication date

  2007-12-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.2478/v10006-007-0044-x
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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