different approach to rule-based machine learning with (LS-1), where learning was viewed as an offline optimization process rather than an online adaptation process. This new approach was more similar to a standard genetic algorithm but evolved independent sets of rules. Since that time LCS methods inspired by the online learning framework introduced by Holland at the University of Michigan have been referred to as Michigan-style LCS, and those inspired by Smith and De Jong at the University of Pittsburgh have been referred to as Pittsburgh-style LCS. In 1986, Holland developed what would be considered the standard Michigan-style LCS for the next decade. Other important concepts that emerged in the early days of LCS research included (1) the formalization of a bucket brigade algorithm (BBA) for credit assignment/learning, (2) selection of parent rules from a common 'environmental niche' (i.e. the match set [M]) rather than from the whole population [P], (3) covering, first introduced as a create operator, (4) the formalization of an action set [A], (5) a simplified algorithm architecture, (6) strength-based fitness, (7) consideration of single-step, or supervised learning problems and the introduction of the correct set [C], (8) accuracy-based fitness (9) the combination of fuzzy logic with LCS (which later spawned a lineage of fuzzy LCS algorithms), (10) encouraging long action chains and default hierarchies for improving performance on multi-step problems, (11) examining latent learning (which later inspired a new branch of anticipatory classifier systems (ACS)), and (12) the introduction of the first Q-learning-like credit assignment technique. While not all of these concepts are applied in modern LCS algorithms, each were landmarks in the development of the LCS paradigm. The revolution Interest in learning classifier systems was reinvigorated in the mid 1990s largely due to two events; the development of the Q-Learning algorithm for reinforcement learning, and the introduction of significantly simplified Michigan-style LCS architectures by Stewart Wilson. Wilson's Zeroth-level Classifier System (ZCS) focused on increasing algorithmic understandability based on Hollands standard LCS implementation. This was done, in part, by removing rule-bidding and the internal message list, essential to the original BBA credit assignment, and replacing it with a hybrid BBA/Q-Learning strategy. ZCS demonstrated that a much simpler LCS architecture could perform as well as the original, more complex implementations. However, ZCS still suffered from performance drawbacks including the proliferation of over-general classifiers. In 1995, Wilson published his landmark paper, "Classifier fitness based on accuracy" in which he