IWLCS 2013 - Sixteenth International Workshop on Learning Classifier Systems

Originally, Learning Classifier Systems (LCSs) were introduced by
John H. Holland as a way of applying evolutionary computation to
machine learning and adaptive behavior problems. Since then, the LCS
paradigm has broadened greatly into a framework that encompasses many
representations, rule discovery mechanisms, and credit assignment
schemes.
Current LCS applications range from data mining, to automated
innovation and the on-line control of cognitive systems. LCS research
includes various actual system approaches: While Wilson's
accuracy-based XCS system (1995) has received the highest attention
and gained the highest reputation, studies and developments of other
LCSs are usually discussed and contrasted. Advances in machine
learning, and reinforcement learning in particular, as well as in
evolutionary computation have brought LCS systems the necessary
competence and guaranteed learning properties. Novel insights in
machine learning and evolutionary computation are being integrated
into the LCS framework.
Thus, we invite submissions that discuss recent developments in all
areas of research on, and applications of, Learning Classifier
Systems. IWLCS is the event that brings together most of the core
researchers in classifier systems. Moreover, a free introductory
tutorial on LCSs is presented the day before the workshop at GECCO
2011. Tutorial and IWLCS workshop thus also provide an opportunity
for researchers interested in LCSs to get an impression of the
current research directions in the field as well as a guideline for
the application of LCSs to their problem domain.
Topics of interests include but are not limited to:
- Paradigms of LCS (Michigan, Pittsburgh, ...)
- Theoretical developments (behavior, scalability and learning bounds, ...)
- Representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
- Types of target problems (single-step, multiple-step, regression/function approximation,...)
- System enhancements (competent operators, problem structure identification and linkage learning, ...)
- LCS for Cognitive Control (architectures, emergent behaviours, ...)
- Applications (data mining, medical domains, bioinformatics, ...)
Submissions and Publication