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. 2013 Feb 26;20(4):603–612. doi: 10.1136/amiajnl-2012-001574

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Outline of the AF-UCS (attribute feedback-sUpervised Classifier System) algorithm. (1) Learning occurs iteratively, focusing on a single training instance from the dataset at a time. (2) Training instance passed to the population of rules (P). (3) Match set (M) is formed, including any rule in (P) that that has a condition matching the attribute states of the instance. (4) Correct set (C) is formed, including any rule in (M) which specifies the correct class of the instance. (5) If no rules are found for (C), randomly generate such a rule using the covering mechanism. (6) Update rule parameters in (M) and (C) (eg, rule fitness). (7) Use rules in (C) to update attribute tracking scores for current instance. (8) The genetic algorithm (GA) selects parent rules from (C) based on fitness and generates offspring rules which are added to (P). If attribute feedback is being used, the attribute tracking scores for the current instance are applied as weights to guide the GA. (9) Deletion mechanism removes rules from (P) based on fitness whenever the size of (P) is greater than the user-specified maximum population size.