Since Learning Classifier Systems (LCSs) were introduced by Holland (1976, 1978) with the aim of creating cognitive systems which used evolutionary computation to learn to perform a certain task by interacting with its environment, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. LCSs are a very active area of research, with interesting, newer approaches that have shown not only to be competitive with respect to state-of-the-art machine learning techniques, but also to be very flexible approaches capable of solving a wide variety of real-world problems that range from data mining to automated innovation and online control. Among the many different approaches, XCS (Wilson, 1995) has recently received a special amount of attention due to its ability to solve problems that previously eluded solution. This year the workshop is presented under the title of Evolutionary Rule-based Machine Learning to broaden the scope and encourage wider participation.
In general, the workshop aims at discussing any advances in the LCS and evolutionary learners fields. Topics of interests include but are not limited to:
The format of the workshop will be set to encourage discussion. After a brief welcome, there will be presentations of the accepted papers followed by a discussion on the presented topic. At the end of the workshop, we will reserve some time to have a round table where we can brainstorm and discuss any LCS topic.