Abstract
Rule-based machine learning focuses on learning or evolving a set of rules that represents the knowledge captured by the system. Due to its inherent complexity, a certain amount of fine tuning is required before it can be applied to a particular problem. However, there is limited information available to researchers when it comes to setting the corresponding run parameter values. In this paper, we investigate the run parameters of Learning Classifier Systems (LCSs) as applied to single-step problems. In particular, we study two LCS variants, XCS for reinforcement learning and UCS for supervised learning, and examine the effect that different parameter values have on enhancing the model prediction, increasing accuracy and reducing the resulting rule set size.