Heck, Dave will do. Who are *we* to be fussy?

Physicist/mathematician David Thomas has boasted that evolution creates information and that he can show this by solving the Steiner tree problem using its powers.

Steiner tree problem: To connect 5 (or whatever) cities with roads using the shortest combined road length.

Thomas has challenged,

If you contend that this algorithm works only by sneaking in the answer (the Steiner shape) into the fitness test, please identify the precise code snippet where this frontloading is being performed.

The guys at Evolutionary Informatics Lab (the one the Baylor dean tried to can years ago, remember?) do exactly that:

The precise code snippet where this frontloading is being performed” from Thomas’s Fortran version of the program is shown below. It ensures that there are at least two interchanges (Thomas calls them variable points) during the initialization of

the population [21]:

NPV = INT(RNDVAL*FLOAT(NVMX-1))+2 ! MINIMUM 2 VARIABLE POINT

Robert J Marks II comments: “In fact, we show the problem attacked by Thomas is pretty lame in comparison with other Steiner tree solutions in the literature.”

Here’s the goods:

“Climbing the Steiner Tree–Sources of Active Information in a Genetic Algorithm for Solving the Euclidean Steiner Tree Problem” (

BIO-Complexity, Vol 2012 )

Winston Ewert, William A Dembski, Robert J Marks II

Genetic algorithms are widely cited as demonstrating the power of natural selection to produce biological complexity. In particular, the success of such search algorithms is said to show that intelligent design has no scientific value. Despite their merits, genetic algorithms establish nothing of the sort. Such algorithms succeed not through any intrinsic prop- erty of the search algorithm, but rather through incorporating sources of information derived from the programmer’s prior knowledge. A genetic algorithm used to defend the efficacy of natural selection is Thomas’s Steiner tree algorithm. This paper tracks the various sources of information incorporated into Thomas’s algorithm. Rather than creating informa- tion from scratch, the algorithm incorporates resident information by restricting the set of solutions considered, introducing selection skew to increase the power of selection, and adopting a structure that facilitates fortuitous crossover. Thomas’s algorithm, far from exhibiting the power of natural selection, merely demonstrates that an intelligent agent, in this case a human programmer, possesses the ability to incorporate into such algorithms the information necessary for successful search.

Follow UD News at Twitter!