Performance and population state metrics for rule-based learning systems

We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the XCS system, and demonstrate their superiority in some cases