but it also generalizes to methods that are not considered to be LCSs (e.g. association rule learning, or artificial immune systems). More general terms such as, 'genetics-based machine learning', and even 'genetic algorithm' have also been applied to refer to what would be more characteristically defined as a learning classifier system. Due to their similarity to genetic algorithms, Pittsburgh-style learning classifier systems are sometimes generically referred to as 'genetic algorithms'. Beyond this, some LCS algorithms, or closely related methods, have been referred to as 'cognitive systems', 'adaptive agents', 'production systems', or generically as a 'classifier system'. This variation in terminology contributes to some confusion in the field. Up until the 2000s nearly all learning classifier system methods were developed with reinforcement learning problems in mind. As a result, the term ‘learning classifier system’ was commonly defined as the combination of ‘trial-and-error’ reinforcement learning with the global search of a genetic algorithm. Interest in supervised learning applications, and even unsupervised learning have since broadened the use and definition of this term. See also Rule-based machine learning Production system Expert system Genetic algorithm Association rule learning Artificial immune system Population-based Incremental Learning Machine learning References External links Video tutorial Learning Classifier Systems in a Nutshell - (2016) Go inside a basic LCS algorithm to learn their components and how they work. Webpages LCS & GBML Central UWE Learning Classifier Research Group Prediction Dynamics Evolutionary algorithms