Distributed Learning Classifier Systems Hai H. Dam, Pornthep Rojanavasu, Hussein A. Abbass, Chris Lokan Artificial Life and Adaptive Robotics Laboratory School of Information Technology and Electrical Engineering The University of New South Wales Australian Defence Force Academy Canberra, Australia {z3140959,s3205770,abbass,cjl}@itee.adfa.edu.au http:://www.itee.adfa.edu.au/˜ alar Abstract. Genetics-based machine learning methods - also called learn- ing 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 incremen- tal learners allowing the system to adapt quickly in dynamic environ- ments; 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 inves- tigating 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. 1 Introduction Pervasive computing has opened a new era of a flat world where people can easily access and/or transfer data around the world. Instead of tons of books, a huge amount of information can be preserved easily within small and affordable electronic systems. Moreover electronic data are produced in greater amounts with a greater frequency than at any time in the past. Data mining, the process of discovering novel and potentially useful patterns in databases [11], has become the most effective method to assist companies and organizations to discover the tacit knowledge hidden in the overwhelming amount of data. In many data mining problems, the ability to understand the discovered knowledge by the mining algorithms is sometimes as important as obtaining an accurate model. For instance, a company might want to profile customers’ expenditures in terms of their consumption, services, location, income, season, etc. The relationship between those features with regards to customers spend- ing habits can answer questions regarding their purchase behaviors. This sort of information might help managers to identify the best segments to target their marketing campaigns. Patterns in a database can be represented in different forms such as neural networks, decision trees, or rule-based systems. It is poten- tially easier to understand patterns represented using the latter form than those for example represented using a neural network.