# No-free-lunch theorem

### From Glossary

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.