A Brief History of Learning Classifier Systems: From CS-1 to XCS
This is an incremental review for researchers in evolutionary computation and machine learning, summarizing existing developments without introducing new methods.
The paper provides a historical overview of the evolution of Learning Classifier Systems, tracing their development from early systems like CS-1 to XCS and subsequent advancements in learning types.
Modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an historical overview of the evolution of such systems up to XCS, and then some of the subsequent developments of XCS to different types of learning.