Editing Learning classifier system (section)

==== Rule/classifier/population ====
A rule is a context dependent relationship between state values and some prediction. Rules typically take the form of an {IF:THEN} expression, (e.g.  {''IF 'condition' THEN 'action'},'' or as a more specific example, ''{IF 'red' AND 'octagon' THEN 'stop-sign'}''). A critical concept in LCS and rule-based machine learning alike, is that an individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Think of a rule as a "local-model" of the solution space.

Rules can be represented in many different ways to handle different data types (e.g. binary, discrete-valued, ordinal, continuous-valued). Given binary data LCS traditionally applies a ternary rule representation (i.e. rules can include either a 0, 1, or '#' for each feature in the data). The 'don't care' symbol (i.e. '#') serves as a wild card within a rule's condition allowing rules, and the system as a whole to generalize relationships between features and the target endpoint to be predicted. Consider the following rule (#1###0 ~ 1) (i.e. condition ~ action).  This rule can be interpreted as: IF the second feature = 1 AND the sixth feature = 0 THEN the class prediction = 1. We would say that the second and sixth features were specified in this rule, while the others were generalized. This rule, and the corresponding prediction are only applicable to an instance when the condition of the rule is satisfied by the instance. This is more commonly referred to as matching. In Michigan-style LCS, each rule has its own fitness, as well as a number of other rule-parameters associated with it that can describe the number of copies of that rule that exist (i.e. the ''numerosity''), the age of the rule, its accuracy, or the accuracy of its reward predictions, and other descriptive or experiential statistics.  A rule along with its parameters is often referred to as a ''classifier''.  In Michigan-style systems, classifiers are contained within a ''population'' [P] that has a user defined maximum number of classifiers. Unlike most [[stochastic]] search algorithms (e.g. [[evolutionary algorithm]]s), LCS populations start out empty (i.e. there is no need to randomly initialize a rule population). Classifiers will instead be initially introduced to the population with a covering mechanism.

In any LCS, the trained model is a set of rules/classifiers, rather than any single rule/classifier.  In Michigan-style LCS, the entire trained (and optionally, compacted) classifier population forms the prediction model.