Distributed Learning Classifier Systems

Genetics-based machine learning methods also called learning classifier systems are evolutionary computation based data mining techniques. The advantages of these techniques are: they are rule-based models providing human-readable learning patterns; they are incremental learners allowing the system to adapt quickly in dynamic environments; and some of them have linear 0(n) learning complexity in the size of dataset. However, not too much effort has yet been made on investigating these techniques in distributed environments. In this chapter, we investigate several issues of evolutionary learning classifier systems for distributed data mining such as knowledge passing in the system, knowledge combination methods at the server, and the effect of numbers of clients on system’s performance.