A No-Free-Lunch theorem for non-uniform distributions of target functions

  title={A No-Free-Lunch theorem for non-uniform distributions of target functions},
  author={Christian Igel and Marc Toussaint},
  journal={J. Math. Model. Algorithms},
The sharpened No-Free-Lunch-theorem (NFL-theorem) states that, regardless of the performance measure, the performance of all optimization algorithms averaged uniformly over any finite set F of functions is equal if and only if F is closed under permutation (c.u.p.). In this paper, we first summarize some consequences of this theorem, which have been proven recently: The number of subsets c.u.p. can be neglected compared to the total number of possible subsets. In particular, problem classes… CONTINUE READING
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