Here’s a great article from Evolution News that explains the trouble that Darwinian evolution has in building up to functional new biological information by using a process of random mutation and natural selection.
Casey Luskin takes a look at a peer-reviewed paper that claims that Darwinian evolution can do the job of creating new information, then he explains what’s wrong with the paper.
In Wilf and Ewens’s evolutionary scheme there is a smooth fitness function. Under this view, there is no epistasis, where one mutation can effectively interact with another to affect (whether positively or negatively) fitness. As a result, any mutations that move the search toward its “target” are assumed to provide an immediate and irrevocable advantage, and are thus highly likely to become fixed. Ewert et al. compare the model to playing Wheel of Fortune:
The evolutionary model that Wilf and Ewens have chosen is similar to the problem of guessing letters in a word or phrase, as on the television game show Wheel of Fortune. They specify a phrase 20,000 letters long, with each letter in the phrase corresponding to a gene locus that can be transformed from its initial “primitive” state to a more advanced state. Finding the correct letter for a particular position in the target phrase roughly corresponds to finding a beneficial mutation in the corresponding gene. During each round of mutation all positions in the phrase are subject to mutation, and the results are selected based on whether the individual positions match the final target phrase. Those that match are preserved for the next round. … After each round, all “advanced” alleles in the population are treated as fixed, and therefore preserved in the next round. Evolution to the fully “advanced” state is complete when all 20,000 positions match the target phrase.
The problem with this approach is that a string of biological information that has only some letters that are part of a useful sequence has no present function, and therefore cannot survive and reproduce.
Thus, Wilf and Ewens ignore the problem of non-functional intermediates. They assume that all intermediate stages will be functional, or lead to some functional advantage. But is this how all fitness functions look? Not necessarily. It’s well known that in many instances, no benefit is derived until multiple mutations are present all at once. In such a case, there’s no evolutionary advantage until multiple mutations are present. The “correct” mutations might occur in parallel, but the odds of this happening are extremely low. Ewert et al. illustrate this problem in the model by using the example of the difficulty of one phrase evolving into another:
Suppose it would be beneficial for the phrase
to evolve into the phrase
What phrase do we get if we simply alternate letters from the two phrases?
Under the assumptions in the Wilf and Ewens model, the “fitness” of this nonsense phrase ought to be exactly half-way between the fitnesses of “all the world is a stage” and “methinks it is like a weasel.” Such a result only makes sense if we are measuring the fitness of the current phrase by its proximity to the target phrase.
But the gibberish of the intermediate phrase doesn’t cause any problem under Wilf and Ewens’s model. Not unlikeRichard Dawkins, they assume that intermediate stages will always yield some functional advantage. And as more and more characters in the phrase match the target, it becomes more and more fit. This yields a nice, smooth fitness function — rich in active information — not truly a blind search.
Not only is there that first problem, but here’s a second:
Wilf and Ewens endowed their mathematical model of evolution with foresight. It is directed toward a target — an advantage that natural selection conspicuously lacks. And what, in our experience, is the only known cause that is goal-directed and has foresight? It’s intelligence. This means that once again, the Evolutionary Informatics Lab has shown that simulations of evolution seem to work only because they’ve been intelligently designed.
This is worth the read. If Darwinian mechanisms really could generate code, then there would be no software engineers. The truth is, the mechanisms don’t work to create new information. For that, you need an intelligent designer.