Editing Learning classifier system (section)

==== Rule discovery/genetic algorithm ====
In the eighth step, LCS adopts a highly elitist [[genetic algorithm]] (GA) which will select two parent classifiers based on fitness (survival of the fittest).  Parents are selected from [C] typically using [[tournament selection]]. Some systems have applied [[roulette wheel selection]] or deterministic selection, and have differently selected parent rules from either [P] - panmictic selection, or from [M]). [[Crossover (genetic algorithm)|Crossover]] and [[Mutation (genetic algorithm)|mutation]] operators are now applied to generate two new offspring rules. At this point, both the parent and offspring rules are returned to [P]. The LCS [[genetic algorithm]] is highly elitist since each learning iteration, the vast majority of the population is preserved. Rule discovery may alternatively be performed by some other method, such as an [[estimation of distribution algorithm]], but a GA is by far the most common approach. Evolutionary algorithms like the GA employ a stochastic search, which makes LCS a stochastic algorithm. LCS seeks to cleverly explore the search space, but does not perform an exhaustive search of rule combinations, and is not guaranteed to converge on an optimal solution.