==Modeling programs that include infrequent, but extreme leaps have proven to greatly shorten the time in identifying the optimum conditions.
From DetectingDesign.com:
It all sounds very good, and quite convincing actually, except perhaps for one little problem. Natural selection is limited in that it can only select, in a positive way, for changes that show improvement in function over what was there before. As it turns out, many mutational changes (i.e., changes in the underlying genetic codes of DNA that dictate how a creature is formed) have absolutely no affect on the function of the organism. Such changes, or mutations, are called “neutral” with respect to functional selectability. There is even a “Neutral Theory of Evolution” proposed fairly recently by Motoo Kimura.
A neutral difference is like “spelling” the code for the same function in a different way. This different spelling still results in producing the same / equal / equivalent result - as I just did by using three different words that mean pretty much the same thing. Or, neutral differences may exist between equally non-meaningful sequences - like the difference between “quiziligook” and “quiziliguck”. Both are equally meaningless when spoken in most situations - right? Therefore, neither has more meaningful or beneficial “fitness” in a given environment as compared with the other. Obviously then, selection between them would be equal or “neutral” with respect to function - i.e., completely random.
Beyond this, most mutations that do happen to affect function do so in a negative way. Natural selection actively works against such mutations to eliminate them from the gene pool over time. These mutational changes are not therefore “beneficial” either.
So, why might this be a problem for evolution? Well, at very low levels of functional complexity (i.e., functions that require a very short sequence of fairly specified genetic real estate to be realized) the ratio of potentially beneficial to non-beneficial sequences is quite high. So, the numbers of non-beneficial differences between one beneficial sequence and the next closest potentially beneficial sequence in sequence space are relatively few.
For example, consider the sequence: cat - hat - bat - bad - big - dig - dog. Here we just evolved from cat to dog where every single character change was meaningful and potentially beneficial in the right environment. It is easy to get between every potential 3-character sequence in the English language system because the ratio between meaningful and non-meaningful in the “sequence space” of 3-character sequences is only about 1 in 18. However, this ratio decreases dramatically, exponentially in fact, with each increase in minimum sequence length. For example, in 7-character sequence space, the ratio is about 1 in 250,000 - and that is not even taking into account the “beneficial” nature of a particular sequence relative to a particular environment/situation. Still, meaningful 7-character sequences are generally very interconnected, like a web made up of thin interconnected roads going around the large pockets of non-meaningful/non-beneficial potential sequences. However, the exponential decrease in the ratio is obvious and the implications are clear. For higher and higher level functions, requiring larger and larger fairly specified sequences to code for them, the ratio of meaningful to meaningless becomes so small so quickly that when more than a few dozen characters are needed the interconnected roadways and bridges that connect various island-clusters of beneficial sequences start to snap apart. At surprisingly low levels of functional complexity this process isolates the tiny islands of beneficial sequences from every other island to such an extent that there is simply no way to reach these tiny isolated islands except to traverse the gap of non-beneficial sequences through a process of purely random change(s) over time.
With every additional step up the ladder of functional complexity, this gap gets wider and wider, in an exponential manner, until it is simply uncrossable this side of trillions upon trillions of years of average time. Natural selection is simply blind when it comes to crossing such gaps. Without the guidance of natural selection, this crossing takes exorbitantly greater amounts of time since the non-beneficial junk sequences of sequence space must be sorted through randomly before a very rare beneficial sequence is discovered by sheer luck (see link):
Trillions upon Trillions of Years - - Not Enough Time
http://www.detectingdesign.com/flagellum.html#Calculation
Not sure about your word based model, but I have used this method in an number of significant design advancements. Think of evolution as representing a slope with the Y axis representing the relative success of an organism to compete within its environment, or to adapt to changes in its environment. Small changes will move the survival index minimally up or down the slope resulting in little evolutionary change. However major changes, as represented by mutations, will move an organism a greater distance up or down the survival index. Most often, mutations are unsuccessful, resulting in the death of the mutation. However, occasionally a mutation will represent a significant advancement and allow the offspring of the mutation to dominate and replace their competitors and predecessors.
HOX genes give us walking/flying/swimming critters with front ends, back ends, tops, bottoms and sides. Probably only a handful of ways you can structure control genes like that ~ and they are incredibly stable platforms.
In fact, let me go further, there's probably only a very small number of possible AND useful HOX genes in the Earth environment.
Now, a quick return to the news ~ listening to Nancy Pelosi tell me a lie about "middle class tax cut". The woman is mentally ill ~ too much botox. It's changing her genome as well ~ she's growing a tail ~ look at her dress in the back when she turns to leave ~ there's something in there and that's not good.