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* 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.<ref name=":11" />
* Flexible application
** Single or multi-step problems
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