There is no RationalWiki without you. We are a small non-profit with no staff – we are hundreds of volunteers who document pseudoscience and crankery around the world every day. We will never allow ads because we must remain independent. We cannot rely on big donors with corresponding big agendas. We are not the largest website around, but we believe we play an important role in defending truth and objectivity. If everyone who saw this today donated $5, we would meet our goal for 2021. |
Fighting pseudoscience isn't free. We are 100% user-supported! Help and donate $5, $20 or whatever you can today with ! |
No Free Lunch Theorems
Part of a convergent series on Mathematics |
1+1=11 |
No Free Lunch Theorems for Search^{[1]} is the title of a 1995 paper of David H. Wolpert and William G. Macready, and No Free Lunch Theorems for Optimization^{[2]} the title of a follow-up from 1997.
In these papers, Wolpert and Macready show that for any algorithm, any elevated performance over one class of problems is offset by performance over another class^{[2]}, i.e. that on average each algorithm works as well as a blind search.
Intelligent Designers love these No Free Lunch Theorems (often abbreviated to NFL), as they seem to indicate that - for instance - evolutionary algorithms have to get some additional information from somewhere to work.
Usually they neglect the fact that an algorithm has to work only on a certain class of functions, and not on all functions. Wolpert himself has repudiated William Dembski's interpretation of the theorems as lending support to intelligent design.
Mathematical formulation of the Sharpened No Free Lunch Theorem[edit]
While the original NFL theorems looked at the class of all functions, the Sharpened NFL theorem is formulated for smaller classes with a special property, i.e., being closed under permutation.
First, some definitions have to be introduced in the way as they seem popular in the literature when talking about the NFL and such. This set of definition can be found - for instance - in a paper^{[3]} of Andrea Valsecchi and Leonardo Vanneschi:
- are finite sets.
- is a function
- a trace of length m over and is a sequence of couples such that and
- a trace is called simple if an element of appears at most once in it
- T is the set of all possible traces over the sets and
- a search operator is a function
- g is non-repeating if (here, for the trace , and )
- if is simple and is non-repeating, then also is a simple trace. Here is the concatenation operator
- a deterministic search algorithm is an application with , where g is a search operator.
- is called non-repeating if g is non-repeating
- for convenience, we'll drop the g and speak of a search-algorithm A.
- a deterministic non-repeating search algorithm will be called algorithm for short
- is the m-th iteration of A to trace t, defined by ,
- , where is the empty trace.
- A set of functions is called closed under permutation (c.u.p) if and for each permutation of (), we have .
Now, at last, the Sharpened NFL:
Sharpened NFL: Let be two deterministic non-repeating search algorithms and let be c.u.p. Then
References[edit]
- ↑ Wolpert, D.H., Macready, W.G. (1995), No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010 (Santa Fe Institute).
- ↑ ^{2.0} ^{2.1} Wolpert, D.H., Macready, W.G. (1997), No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation 1, 67. http://ic.arc.nasa.gov/people/dhw/papers/78.pdf
- ↑ Andrea Valsecchi and Leonardo Vanneschi (2008), A study of Some Implications of the No Free Lunch Theorem, Applications of Evolutionary Computing, Volume 4974/2008, Springer Berlin/Heidelberg, pp. 634-635