Perhaps Not a Free Lunch But At Least a Free Appetizer

This work is a product of the Collaborative Research Center 531, \Computational Intelligence", at the University of Dortmund and was printed with nancial support of the Deutsche Forschungsgemeinschaft. Abstract It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in particular, in situations where not much is known about the objective function to be optimized. In contrast to that Wolpert and Macready (1997) proved that all optimization techniques have the same behavior | on average over all f : X ! Y where X and Y are nite sets. This result is called No Free Lunch Theorem. Here diierent scenarios of optimization are presented. It is argued why the scenario on which the No Free Lunch Theorem is based does not model real life optimization. For more realistic scenarios it is argued why optimization techniques diier in their eeciency. For a small example this claim is proved.