Existence of a general-purpose (almost) universal optimization strategy From Wikipedia about interpretations of no free lunch theorem

A conventional, but not entirely accurate, interpretation of the NFL
  results is that "a general-purpose universal optimization strategy
  is theoretically impossible, and the only way one strategy can
  outperform another is if it is specialized to the specific problem
  under consideration".[14] Several comments are in order:
A general-purpose almost-universal optimizer exists theoretically. Each search algorithm performs well on almost all
  objective functions.[11]
...
For almost all objective functions, specialization is essentially accidental. Incompressible, or Kolmogorov random,
  objective functions have no regularity for an algorithm to exploit.
  Given an incompressible objective function, there is no basis for
  choosing one algorithm over another. If a chosen algorithm performs
  better than most, the result is happenstance.[11]

I was wondering 


*

*How shall I understand that a general-purpose universal optimizer
doesn't exist but a general-purpose almost universal optimizer
does? What differences are between universal and almost
universal?

*Do the last two sentences in bold contradict each other? Do "an
optimizer being almost-universal and perform well on all objective
functions" and  "it being specialized for almost all objective
functions" imply each other?


Thanks and regards!
 A: The first quote should not have been published, because the conclusion is categorically wrong. Almost all objective functions are Kolmogorov random. They have no order whatsoever that can be exploited in optimizer design. An optimizer cannot be specialized to one of them. For almost all of the theoretically possible functions on which an optimizer performs well (or poorly), performance has absolutely nothing to do with specialization (or lack thereof).


*

*There is "no free lunch" when the optimization result does not, as far as the practitioner can tell, depend on the choice of sampler. This does not mean that the optimization result is bad. When all objective functions are equally likely, modest-sized samples of the objective function, drawn uniformly at random, almost always contain a good value. Because drawing a sample uniformly at random is equivalent to drawing a deterministic (non-random) algorithm uniformly at random, and then applying it, almost all deterministic algorithms perform well on each objective function. See Table 1, p. 16, of the emended version of my first NFL paper (1996).

*The last boldface sentence is self-contradictory, and should be deleted. What I have written here explains why, as do the sentences following it in the article.
