Both in comments, and via email, I've received numerous requests to take a look at the work of Dembski and Marks, published through Professor Marks's website. The site is called the "Evolutionary Informatics Laboratory". Before getting to the paper, it's worth taking just a moment to understand its provenance - there's something deeply fishy about the "laboratory" that published this work. It's not a lab - it's a website; it was funded under very peculiar circumstances, and hired Dembski as a "post-doc", despite his being a full-time professor at a different university. Marks claims that his work for the EIL is all done on his own time, and has nothing to do with his faculty position at the university. It's all quite bizarre. For details, see here.
On to the work. Marks and Dembski have submitted three papers. They're all in a very similar vein (as one would expect for three papers written in a short period of time by collaborators - there's nothing at all peculiar about the similarity). The basic idea behind all of them is to look at search in the context of evolutionary algorithms, and to analyze it using an information theoretic approach. I've picked out the first one listed on their site: Conservation of Information in Search: Measuring the Cost of Success
There's two ways of looking at this work: on a purely technical level, and in terms of its presentation.
On a technical level, it's not bad. Not great by any stretch, but it's entirely reasonable. The idea of it is actually pretter clever. They start with NFL. NFL says, roughly, that if you don't know anything about the search space, you can't select a search that will perform better than a random walk. If we have a search for a given search space that does perform better than a random walk, in information theoretic terms, we can say that the search encodes information about the search space. How can we quantify the information encoded in a search algorithm that allows it to perform as well as it does?
So, for example, think about a search algorithm like Newton's method. It generally homes in extremely rapidly on the roots of a polynomial equation - dramatically better than one would expect in a random walk. For example, if we look at something like y = x2 - 2, starting with an approximation of a zero at x=1, we can get to a very good approximation in just two iterations. What information is encoded in Newton's method? Among other things, it's working in a Euclidean space on a continuous, differentiable curve. That's rather a lot of information. We can actually quantify that in information theoretic terms by computing the average time to find a root in a random walk, compared to the average time to find a root in Newton's method.
Further, when a search performs worse than what is predicted by a random walk, we can say that with respect to the particular search task, that the search encodes negative information - that it actually contains some assumptions about the locations of the target that actively push it away, and prevent it from finding the target as quickly as a random walk would.
That's the technical meat of the paper. And I've got to say, it's not bad. I was expecting something really awful - but it's not. As I said earlier, it's far from being a great paper. But technically, it's reasonable.
Then there's the presentation side of it. And from that perspective, it's awful. Virtually every statement in the paper is spun in a thoroughly dishonest way. Throughout the paper, they constantly make statements about how information must be deliberately encoded into the search by the programmer. It's clear the direction that they intend to go - they want to say that biological evolution can only work if information was coded into the process by God. Here's an example from the first paragraph of the paper:
That's the first one, and the least objectionable. But just half a page later, we find:
The significance of COI [MarkCC: Conservation of Information - not Dembski's version, but from someone named English] has been debated since its popularization through the NFLT . On the one hand, COI has a leveling effect, rendering the average performance of all algorithms equivalent. On the other hand, certain search techniques perform remarkably well, distinguishing themselves from others. There is a tension here, but no contradiction. For instance, particle swarm optimization  and genetic algorithms ,  perform well on a wide spectrum of problems. Yet, there is no discrepancy between the successful experience of practitioners with such versatile search algorithms and the COI imposed inability of the search algorithms themselves to create novel information , , . Such information does not magically materialize but instead results from the action of the programmer who prescribes how knowledge about the problem gets folded into the search algorithm.
That's where you can really see where they're going. "Information does not magically materialize, but instead results from the action of the programmer". The paper harps on that idea to an inappropriate degree. The paper is supposedly about quantifying the information that makes a search algorithm perform in a particular way - but they just hammer on the idea that the information was deliberately put there, and that it can't come from nowhere.
It's true that information in a search algorithm can't come from nowhere. But it's not a particularly deep point. To go back to Newton's method: Newton's method of root finding certainly codes all kinds of information into the search - because it was created in a particular domain, and encodes that domain. You can actually model orbital dynamics as a search for an equilibrium point - it doesn't require anyone to encode in the law of gravitation; it's already a part of the system. Similarly in biological evolution, you can certainly model the amount of information encoded in the process - which includes all sorts of information about chemistry, reproductive dynamics, etc.; but since those things are encoded into the universe, you don't need to find an intelligent agent to have coded them into evolution: they're an intrinsic part of the system in which evolution occurs. You can think of it as being like a computer program: computer programs don't need to specifically add code into a program to specify the fact that the computer they're going to run on has 16 registers; every program for the computer has that wired into it, because it's a fact of the "universe" for the program. For anything in our universe, the basic facts of our universe - of basic forces, of chemistry, are encoded in their existence. For anything on earth, facts about the earth, the sun, the moon - are encoded into their very existence.
Dembski and Marks try to make a big deal out of the fact that all of this information is quantifiable. Of course it's quantifiable. The amount of information encoded into the structure of the universe is quantifiable too. And it's extremely interesting to see just how you can compute how much information is encoded into things. I like that aspect of the paper. But it doesn't imply anything about the origin of the information: in this simple initial quantification, information theory cannot distinguish between environmental information which is inevitably encoded, and information which was added by the deliberate actions of an intelligent agent. Information theory can quantify information - but it can't characterize its source.
If I were a reviewer, would I accept the paper? It's hard to say. I'm not an information theorist; so I could easily be missing some major flaw. The style of the paper is very different from any other information theory paper that I've ever read - it's got a very strong rhetorical bent to it which is very unusual. I also don't know where they submitted it, so I don't know what the reviewing standards are - the reviewing standards of different journals are quite different. If this were submitted to a theoretical computer science journal like the ones I typically read, where the normal ranking system is (reject/accept with changes and second review/weak accept with changes/ strong accept with changes/strong accept), I would probably rank it either "accept with changes and second review" or "weak accept with changes".
So as much as I'd love to trash them, a quick read of the paper seems to show that it's a mediocre paper, with an interesting idea. The writing sucks: it was written to try to make a point that it can't make technically, and it makes that point with all the subtlety of a sledgehammer, despite the fact that the actual technical content of the paper can't support it.