Automatic generation of fuzzy logic rule bases: Example I

Learning fuzzy rule-based systems with genetic algorithms can lead to very useful descriptions of several problems. Many different alternative descriptions can be generated. In many cases, a simple rule base similar to rule bases designed by humans is preferable since it has a higher possibility of being valid in unforeseen cases. Thus, the main idea of this paper is to study the genetic fuzzy rule base learning algorithm FRBL [1] by examples from the machine learning repository [2] and to compare it with some other approaches.