No-free-lunch theorem

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In heuristic search, this is the proposition that all methods perform the same when averaged over all possible objective functions. The idea is that a particular search algorithm, like simulated annealing, may be designed to perform especially well for some functions, compared to a genetic algorithm, but when applied to a representative sample of all possible costs, they will perform exactly the same, on the average. This implies that to do especially well on some problems, the search method must do worse on others; hence, the "no-free-lunch" description.


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