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The No Free Lunch Theorems and Their Application to Evolutionary Algorithms
By Mark
Perakh
Posted January 6,
2003
H. Allen Orr published a very eloquent critique [1] of Dembski's book No
Free Lunch [2]. There are many fine points in Orr's critique
elucidating inconsistencies and unsubstantiated assertions by Dembski. Among his
critical comments Orr emphasizes what is, in his view, the most serious flaw in
Dembski's position, specifically in Dembski's use of the No Free Lunch theorems
as allegedly proving the impossibility of Darwinian evolution. Orr writes about
the NFL theorems: "The NFL theorems compare the efficiency of evolutionary
algorithms; roughly speaking, they ask how often different search algorithms
reach a target within some number of steps." Later Orr offers his explanation of
why Dembski's use of the NFL theorems is wrong, "The problem with all this is so
simple that I hate to bring it up. But here goes: Darwinism isn't trying to
reach a prespecified target." He says further, "Evolution isn't searching for
anything and Darwinism is not therefore a search algorithm."
While essentially Orr's critical remarks are well taken, their formulation was
insufficiently careful from a mathematician's viewpoint so it may create a
distorted impression of what Orr had in mind. Indeed, in a private communication
Orr, who is a biologist, provided an explanation of his actual views on the
subject of the NFL theorems' application to Darwinian evolution, and these views
are essentially in accord with a proper interpretation of the NFL
theorems. Since, however, the rendition of that problem in Orr's review of
Dembski's book may be confusing for unprepared readers, I will try to clarify
the problem by first explaining in way as simple as possible the NFL
theorems, and second, briefly discussing their misapplication by Dembski.
Contrary to Dembski's assertions, the NFL theorems do not at all prohibit
Darwinian evolution (or make evolutionary algorithms in general incapable of
outperforming a random sampling – which is Demsbki's position). Orr is
correct in stating that, but his argument requires a slight clarification.
Dembski twice replied to Orr. In his first, relatively brief reply [3]
Dembski did not even mention the remark which Orr suggested as his main point
for rejecting Dembski's position. In his second, very lengthy response [4]
Dembski discussed at length a myriad of points having little to do with Orr's
main critical comment and only toward the end of his response briefly related to
that point. Dembski's rebuttal of Orr's comment essentially boils down to the
assertion that Darwinian evolution is after all a targeted process. He writes,
"Darwinism is at least in part about finding specified targets (albeit not
prespecified targets)..."
Since the difference between targets simply "specified" and "prespecified" does
not convert them into "nontargets," this statement contradicts Dembski's
earlier assertions, such as, for example, found on page 193 of his book [2]: "Not only does this algorithm introduce a teleology foreign to the
natural world (and certainly to the Darwinian mechanism) but it also
introduces a teleology foreign to the evolutionary algorithms actually used by
working computer scientists." (Italics are mine. MP). Either Darwinian
evolution does have targets (i.e. is teleological) or it does not. The very
concept of a target entails its preselection. What is a target which is
not preselected is one of Dembski's secrets.
It looks like, in his quest for rebutting Orr's critique, Dembski resorted to
notions which, first, contradict his own position stated previously and, second,
are fallacious since Darwinian evolution, as Orr correctly states, is certainly
a nontargeted process. On the other hand, Dembski (and all of his admirers and
supporters) have so far missed a real vulnerable point in Orr's wording
which may be construed as misinterpreting certain features of the NFL theorems.
Dembski's conclusions from the NFL theorems are indeed unsubstantiated since
these theorems, contrary to Dembski's assertion, in no way mean that
evolutionary algorithms cannot outperform a random sampling. To see that this is
indeed so, let us briefly discuss the NFL theorems [5].
I'll do this without using mathematical symbolism.
Imagine two search algorithms conducting a search on a given fitness landscape.
They move from point to point over the search space (choosing the search points
either at random or in a certain order). Each algorithm conducts a certain
number of iterations. After having performed, say, m measurements,
an algorithm produces what Wolpert and Macready call a "sample." A sample is in
fact simply a table wherein the values of the fitness function at each of
m search points are listed.
Let us ask the question: if any two algorithms have searched the same number of
points, will the two samples they produce be identical? The answer can only be
given in probabilistic terms. Generally speaking, the samples cannot be expected
to be identical for any two arbitrarily chosen algorithms. The probability of
algorithm A producing a specific table which is m rows long is
different from the probability of algorithm B producing the same table after the
same number of iterations.
Now enter the first NFL theorem. It asserts that if the results of the two
algorithms' searches are compared not for a specific fitness landscape but
averaged over all possible landscapes, the probabilities of obtaining the
same sample, i.e. the same table m rows long, are equal for any
pair of algorithms. Remember that the quantity which is averaged is the
probability of generating a given sample by an algorithm.
This is a literally exact translation into plain words of the statement of the
first NFL theorem from its mathematically symbolic form.
The NFL theorems do not restrict the value of m – the number of
iterations – in any way. The size of a table which constitutes a sample may be
as small or as large as one may choose. There is no condition that the search
stops when a certain preselected number of iterations have been completed, or
when a preselected value of the fitness function has been found. The NFL
theorems are valid regardless of how many times the algorithms probe the search
space, or what the measured values of the fitness function happen to be.
As we see, the concept of a target is absent from the NFL theorems.
However, the NFL theorems do not forbid the algorithms to be targetoriented
either. These theorems are indifferent to algorithms' having or not having a
target.
Since the NFL theorems are often discussed in terms of algorithms' performance,
what is the relation between the literal meaning of the NFL theorems and the
algorithms' "performance"? Here we enter the realm of consequences and
interpretations of the NFL theorems.
One of the corollaries relates to what is called a performance measure.
The concept of a performance measure is not part of the NFL theorems as
such. It is introduced within the framework of consequences from the NFL
theorems. The NFL theorems are usually (and reasonably) interpreted in a way
allowing for a wide latitude in the choice of performance measures. The NFL
theorems remain valid regardless of the choice of performance measures. In
particular, whereas the NFL theorems as such do not incorporate the concept of a
target of a search, the performance measure may or may not incorporate that
concept. Without contradicting the NFL theorems, the algorithms in question may
be either targetoriented or not. The choice between the two alternatives enters
the discourse only on the level of performance measures. The assertion of the
NFL theorems which literally is about the probability of algorithms' generating
a given sample, is interpreted as a consequent assertion about algorithms'
"performance."
Here are examples of both targeted and nontargeted algorithms, both equally
subject to the NFL theorems.
Assume that the fitness function is simply the height of peaks in a specific
mountainous region. If we choose a targetoriented algorithm, the target of the
search can be defined as a specific peak P whose height is, say 6,000 meters
above the sea level. In this case the number of iterations required to reach the
predefined height of 6,000 meters (or perhaps more convenient its reciprocal
value) can serve as the performance measure. Then algorithm A performs better
than algorithm B if A converges on the target in fewer steps than B. Obviously,
if two algorithms generated the same sample after m iterations,
this would mean that they both find the target – peak P – also after the same
number of iterations. The first NFL theorem tells us that the average
probabilities of reaching peak P in m steps are the same
for any two algorithms. In other words, in probabilistic terms, the statement of
the 1st NFL theorem translates into the statement of equal average
performance of any two algorithms if averaging is over all possible fitness
landscapes (not all of which all must necessarily exits materially). This is
interpreted in the above example as the statement that the average number of
iterations required to locate the target is the same for any two algorithms if
the averaging is done over all possible mountainous landscapes.
The NFL theorems do not say anything however about the relative performance of
algorithms A and B on a specific landscape where either A or B can happen
to be much better than its competitor.
The competition of algorithms can occur as well in a targetless environment and
the NFL theorems will be valid for this case as well. For example, rather than
defining a target as a certain peak P, or even as a peak of a certain
height, the algorithms may be compared by finding out which of them, A or B,
finds simply a higher peak after a certain (arbitrarily chosen) number of
iterations. The performance measure in this case is the height of a peak reached
after m iterations. No specific peak and no specific height is
preselected as a target. Algorithm A that after m iterations
finds a higher peak than algorithm B performs better. The 1^{st}
NFL theorem tells us that, if averaged over all possible mountainous reliefs
(not all of them necessarily materially existing) the probabilities of
both A and B generating the same sample after m iterations are
equal. Consequently, this may be reasonably interpreted as a statement that in
all likelihood the height of a peak reached after m iterations, if
averaged over all possible landscapes, will be the same for any two
algorithms.
The conclusions in both reviewed situations, while not directly asserted in the
NFL theorems, are consequences of the 1st NFL theorem based on its
reasonable interpretation. The 1st NFL theorem asserts the equal
probabilities of any two algorithms generating a given sample (i.e. identical
tables containing the values of the fitness function measured at m
points). It is (reasonably) interpreted as the equal values of
performance measures for any two algorithms if averaged over all possible
landscapes.
Whatever performance measure is chosen is secondary and does not invalidate the
NFL theorems. These theorems are equally valid for both targeted and
nontargeted searches. They are valid for evolutionary algorithms all right. (As
Wolpert and Macready have proven recently – see [6, 7]  the NFL theorems may be invalid for coevolutionary algorithms,
but this is a different story). Orr's assertion, because of its formulation,
could unfortunately be interpreted as an assertion according to which the NFL
theorems do not apply to Darwinian algorithms because the latter are targetless
and hence are "not search algorithms" in the NFL sense. In such an unfortunate
interpretation Orr's assertion would be incorrect and therefore Orr's statement
requires the above clarification.
What is true, though, is that the NFL theorems, while perfectly applicable to
all kinds of algorithms including the Darwinian evolutionary algorithms (with a
possible exception for coevolution), contrary to Dembski's assertions, do
not in any way prohibit Darwinian evolution. The NFL theorems do not
at all prevent evolutionary algorithms from outperforming a random sampling (or
blind search) because these theorems are about performance averaged over all
possible fitness functions. They say nothing about performance of different
algorithms on specific fitness landscapes. In reallife situations, it is the
performance on a specific landscape that counts and this is where evolutionary
algorithms routinely outperform random searches and do so very efficiently, both
when the processes are targeted (as in Dawkins's algorithm –see [8]) and when they are nontargeted (as Darwinian evolution is).
In writing this brief essay I had fruitful discussions with Brendan McKay whose
contribution is gratefully acknowledged. Helpful comments have also been
suggested by Erik Tellgren. H. Allen Orr in a series of email messages
kindly clarified his views on the discussed subjects.
References
[1] Orr, H. Allen. 2002. Review of William Dembski's No Free
Lunch. Boston Review. Summer issue. See also online, accessed on December 22,2002.
[2] Dembski, William A. 2002. No Free Lunch. Why Specified Complexity Cannot be Purchased Without Intelligence. (Lanham, Maryland: Rowman and Littlefield Publishers).
[3] Dembski, William A. 2002 b. Sheer versus Real Possibilities.
Boston Review, OctoberNovember. Also online, accessed on December 26,2002.
[4] Dembski, William. 2002 c. Evolution's Logic of Credulity:
An Unfettered Response to Allen Orr. Online. Accessed on December 26, 2002.
[5] Wolpert, David H and William G. Macready. 1997. The No Free Lunch Theorems For Optimization. IEEE Trans.Evol. Comp. v.1, no 1, 6782.
[6] Wolpert, David H. 2002. Online. Accessed on January 6, 2003.
[7] Wolpert, David H. and William G. Macready. 2003 (to be published).
[8] Dawkins, Richard. 1996 [1986]). The Blind Watchmaker.
Why the Evidence of Evolution Reveals a Universe Without Design. (New
York: W.W. Norton).
Mark Perakh's home page
Comments: marperak@cox.net

