rules where the genetic algorithm operates at the level of individual rules and the solution is represented by the entire rule population. Michigan style systems also learn incrementally which allows them to perform both reinforcement learning and supervised learning, as well as both online and offline learning. Michigan-style systems have the advantage of being applicable to a greater number of problem domains, and the unique benefits of incremental learning. Pittsburgh-Style Learning Classifier System Pittsburgh-Style LCSs are characterized by a population of variable length rule-sets where each rule-set is a potential solution. The genetic algorithm typically operates at the level of an entire rule-set. Pittsburgh-style systems can also uniquely evolve ordered rule lists, as well as employ a default rule. These systems have the natural advantage of identifying smaller rule sets, making these systems more interpretable with regards to manual rule inspection. Hybrid systems Systems that seek to combine key strengths of both systems have also been proposed. Advantages Adaptive: They can acclimate to a changing environment in the case of online learning. Model free: They make limited assumptions about the environment, or the patterns of association within the data. They can model complex, epistatic, heterogeneous, or distributed underlying patterns without relying on prior knowledge. They make no assumptions about the number of predictive vs. non-predictive features in the data. Ensemble Learner: No single model is applied to a given instance that universally provides a prediction. Instead a relevant and often conflicting set of rules contribute a 'vote' which can be interpreted as a fuzzy prediction. Stochastic Learner: Non-deterministic learning is advantageous in large-scale or high complexity problems where deterministic or exhaustive learning becomes intractable. Implicitly Multi-objective: Rules evolve towards accuracy with implicit and explicit pressures encouraging maximal generality/simplicity. This implicit generalization pressure is unique to LCS. Effectively, more general rules, will appear more often in match sets. In turn, they have a more frequent opportunity to be selected as parents, and pass on their more general (genomes) to offspring rules. Interpretable:In the interest of data mining and knowledge discovery individual LCS rules are logical, and can be made to be human interpretable IF:THEN statements. Effective strategies have also been introduced to allow for global knowledge discovery identifying significant features, and patterns of association from the rule population as a whole. Flexible application Single or multi-step problems Supervised, Reinforcement or Unsupervised Learning Binary Class and Multi-Class Classification Regression Discrete or continuous