Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system

Bloat control and generalization pressure are very important issues in the design of Pittsburgh Approach Learning Classifier Systems (LCS), in order to achieve simple and accurate solutions in a reasonable time. In this paper we propose a method to achieve these objectives based on the Minimum Description Length (MDL) principle. This principle is a metric which combines in a smart way the accuracy and the complexity of a theory (rule set , instance set, etc.). An extensive comparison with our previous generalization pressure method across several domains and using two knowledge representations has been done. The test show that the MDL based size control method is a good and robust choice.