|Winston Ewert||William Dembski||Robert Marks|
Хорошая ID-шная статья, объясняющая, почему то, что так наз. эволюционные алгоритмы эффективно работают на практике, нельзя рассматривать как модель эволюции в действии.
The claim about genetic algorithms is not they have produced a hidden answer, like a bird-watcher using a precise set of coordinates to locate a bird. Instead, the claim is that the bird-watcher has been provided with details about the habitat and behavior of birds. This information assists in his search, enabling him to find the birds he wants to watch.
The Darwinist claim is that no such assistance is required. Rather, natural selection is innately capable of solving any biological problem that it faces. Analogously, a genetic algorithm ought to be able to succeed given nothing more than the description of the problem faced. It should not be necessary for an intelligent agent to tune or direct the evolutionary process. Any process so tuned is a teleological process, not a naturalistic one. The argument from genetic algorithms depends on maintaining the ateleological status.
Genetic algorithms typically succeed because programmers incorporate problem-specific knowledge into the search algo-rithm. Various examples have been published in the literature. Avida , a program purported to demonstrate evolution, works by rewarding simpler versions of complex components . Dawkin’s “METHINKS IT IS LIKE A WEASEL” simulation  works by providing the distance to a target phrase . Ev , another program purported to demonstrate evolution, works by providing the distance to a target along with a biased genomic representation . Such algorithms do not demonstrate the abilities of undirected processes, but rather the powerful combination of human intelligence and brute force computing power.
[S]uccess is due to prior knowledge being exploited to produce active information in the search algorithm. Although the code does not include the actual Steiner shape, it does include a tuned algorithm for how to find the Steiner shape.
It should be emphasized that fine-tuning genetic algorithms is common practice. There is nothing unreasonable about the practice. It is in fact necessary, and very useful for producing results from genetic algorithms. Problems arise when attempting to draw inference from a fine-tuned simulation to non-fine-tuned biological reality. We will demonstrate that the fine-tuning is necessary to the success of the algorithm; consequently, the results cannot be used to defend the success of search algorithm in the absence of fine-tuning.
Это как раз то, о чём я писал здесь.