This thesis has introduced and investigated a new kind of rule-based evolutionary online learning system. It addressed the problem of distributing the knowledge of a Learning Classifier System, that is represented by a population of classifiers. The result is a XCS-derived Learning Classifier System 'XCS with Distributed Rules' (XCS-DR) that introduces independent, interacting agents to distribute the system's acquired knowledge evenly. The agents act collaboratively to solve problem instances at hand. XCS-DR's design and architecture have been explained and its classification performance has been evaluated and scrutinized in detail in this thesis. While not reaching optimal performance, compared to the original XCS, it could be shown that XCS-DR still yields satisfactory classification results. It could be shown that in the simple case of applying only one agent, the introduced system performs as accurately as XCS.