The book "Rule-Based Evolutionary Online Learning Systems" by Martin V. Butz offers a comprehensive analysis and in-depth understanding of rule-based evolutionary online learning systems, also known as Michigan-style learning classification systems (LCSs). These systems combine the strengths of reinforcement learning with the generative capabilities of genetic algorithms, promising a flexible, online learning system that is solely based on reinforcement. Despite their promising applications and interesting connections to animal learning and cognition, the understanding of these systems has long been unclear. The book pursues three main objectives: establishing a facet-based approach to the analysis and design of LCSs, improving the XCS classifier system, and identifying the advantages and application possibilities of LCS-based learning approaches. By achieving these goals, a rigorous understanding of how LCSs operate is promoted, enabling their successful application in various problem areas.