Editing Learning classifier system (section)

== Variants ==

=== Michigan-Style Learning Classifier System ===
Michigan-Style LCSs are characterized by a population of 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.