BIO-Complexity, an open-access peer reviewed science journal that focuses on the debate over design, enters its third volume with a paper from the team at Baylor University led by Bob Marks, one of our affiliated scientists at Biologic Institute ("Climbing the Steiner Tree -- Sources of Active Information in a Genetic Algorithm for Solving the Euclidean Steiner Tree Problem").

Like prior work from that team, the new paper by Winston Ewert, Bill Dembski and Bob Marks shows that computational implementations of the Darwinian mechanism only work in a very limited sense. In order to outperform random guessing, they have to be tailored to suit the specific problem of interest. In other words, for each problem to be tackled, someone who understands the best way to go about solving it has to construct a special version of a mutation-selection algorithm if that algorithm is going to be of any help.

That plainly contradicts the Darwinian idea that mutation and selection is a one-size-fits-all problem solver.

Furthermore, if you dig a bit beyond these papers and look at what kinds of problems this technique is being used for in the engineering world, you quickly find that it is of extremely limited applicability. It works for tasks that are easily accomplished in a huge number of specific ways, but where someone would have to do a lot of mindless fiddling to decide which of these ways is best.

That's helpful in the sense that we commonly find computers helpful -- they do what we tell them to do very efficiently, without complaining. But in biology we see something altogether different. We see elegant solutions to millions of engineering problems that human ingenuity cannot even begin to solve.

Cross-posted at Biologic Perspectives.