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A No-Free-Lunch Theorem for Non-Uniform Distributions of Target Functions

2004
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Journal of Mathematical Modelling and Algorithms
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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

doi:10.1023/b:jmma.0000049381.24625.f7
fatcat:zt5wtywjq5ds3ipxqqzoq5ztga