Monkey-Man Hypothesis Thwarted by Mutation Rates

and

updated version

(did you put the actual code up anywhere)?
The code for the original version is here:
http://www.duke.edu/~pat7/publ.....rdLength.m

All the code used in the second version is here:
http://www.duke.edu/~pat7/public/htm/source/

The vast majority of that code is for handling the more complicated dictionary searches. The only GA-related function is here:

http://www.duke.edu/~pat7/publ.....bination.m

enjoy!

“The chance of fixation of a selectively advantageous mutation is, regardless of the educational background of the proponent and impressiveness of their claims, 2s (2 x the selection coefficient). “”
PaV, seriously, man: the chance of fixation (=2s for a beneficial mutation in a large, free-breeding population) is different from time of fixation (=(2/s)ln(2N)) which is the number of generations that it takes, on average, for a mutation that reaches fixation to do so. Go back and read what I wrote.

To be explicit, just in case: a new mutation with a selection coefficient of 0.01 (small: in human terms it would mean an average of 1 more descendant over 50 generations) in a large population (say, 1,000,000 individuals) has a chance of fixation of 2%. That is, it will have to appear on average 50 times before one gets fixed. OK? Good. Now, when it gets fixed, the time it takes to reach fixation (i.e. to sweep the population) will be, on average, (2/0.01)ln(2,000,000)=~2,900 generations.

Now, the evolution of cats was certainly driven by humans, and it certainly involved the fixation of many mutations affecting reproductive and behavioral features of the animal. Many genetic differences, for instance, are known to exist between domestic cats and the Ethiopian wild cat, which is thought to be their wild ancestor. That said, neither the Egyptians nor anyone else since was trying to “evolve a new species”. Of course, the selection coefficients during artificial selection are much stronger. If you work on a small enough population, you can reach fixation of certain alleles in two generations. I do it in my own lab to generate purely mutant mouse strains.

“If you’ll read the first post that Allen MacNeil wrote in the “We is Junk” thread,http://www.uncommondescent.com/archives/1777, you’ll see that evolutionary biologists have pretty much given up population genetics as a way of explaining evolution.”
I am sure that will come to a surprise to Dr. McNeill, and all biologists for that matter. I suggest you go read what you wrote again, paying attention to his words.

The chance of fixation of a selectively advantageous mutation is, regardless of the educational background of the proponent and impressiveness of their claims, 2s (2 x the selection coefficient). An only slightly advantageous mutation, with a selective coefficient of 0.01 (i.e. an increase in 1% in transmission – essentially experimentally undetectable in humans) has a chance of fixation of 2%.

I’m afraid this doesn’t obviate your problem. You’re now saying it will take 2,000 years to generate a new “species”. The Egyptians lived over 3,000 years ago, and the wild cats that lived then, are still the same today.

And, yes, you’ve finally have gotten the right formula for time to fixation, but that’s not what you said before:

The chance of fixation of a selectively advantageous mutation is, regardless of the educational background of the proponent and impressiveness of their claims, 2s (2 x the selection coefficient). An only slightly advantageous mutation, with a selective coefficient of 0.01 (i.e. an increase in 1% in transmission – essentially experimentally undetectable in humans) has a chance of fixation of 2%.

If you’ll read the first post that Allen MacNeil wrote in the “We is Junk” thread,http://www.uncommondescent.com/archives/1777, you’ll see that evolutionary biologists have pretty much given up population genetics as a way of explaining evolution.

(This should also answer PaV’s previous comment about favorable mutations becoming fixed since Darwin’s times.)

Also, despite the fact that I’m looking for any 10 letter word, there are only on the order of 10-20k of them (depending on how you count), and there are 26^10 or 1.4*10^14 possible 10-letter combinations, so the odds of picking any particular word of length 10 at random is still very low (about 1.4*10^-10).

That’s actually why I don’t find the results interesting. 1.4*10^10-10 doesn’t even approach the Universal Probability Bound of 1*10^-50 proposed by French mathematician Emile Borel.

As for calculating the informational bits, here is an example: “ME THINKS IT IS LIKE A WEASEL” is only 133 bits of information(when calculated as a whole sentence; the complexity of the individual items of the set is 16, 48, 16, 16, 32, 8, 48 plus 8 bits for each space). So aequeosalinocalcalinoceraceoaluminosocupreovitriolic would be 416 informational bits. Even though that’s not 500 I’d still be surprised if that showed up with the way your GA is designed right now.

But even if we assume (out of whatever calculation Spetner did) a rate of appearance of a specific mutation in a population of 1/600, that means that every few generations one of the 500 mutations he presupposes are necessary for the speciation would likely appear . And every new generation would be another roll, with another chance of another of the gene mutations to appear, and all of them would travel to the population in parallel, working through fixation.

The problem here, Andrea, is that if you really believe that these mutations travel forward in parallel, that means that in about 500 generations, a new species will appear. That’s 500 years for most animals. Are you aware of a new species of cat, or dog, or horse, or……well, fill in the blank. I thought evolution takes place too slowly for us to see it in action. Descriptions of cats from the Egyptians dynasties is the same as for current day species. And, please, don’t appeal to this being attributable to artificial selection, because the selection pressure of artifical selection is much higher than that found in nature.

And, if it takes 500 years for a new “species” to come about, then why don’t we see them in the fossil record.

I await your answer.

This is interesting, and it does a good job of showing that blind search is infeasible as an approach to generating complicated text.

For fun see if your program can hit upon pseudopseudohypoparathyroidism (30 letters) or aequeosalinocalcalinoceraceoaluminosocupreovitriolic (52 letters).

I can pretty much guarantee you that the odds of finding any one of these particular words is vanishingly small. Of course, the odds of finding any particular 10 letter word is also very small, but not nearly as small as for the other words you suggested.

I’m glad to see your program doesn’t “sneak in too much information” considering the fitness function only checks for a 10-character string, although the target is very large considering you’re looking for ANY 10-letter word.

Actually, the fitness function itself is defined as:
Fit(word) =
length(word) iff word is in dictionary
0 otherwise

I did implement a stopping criterion of “let me know when you hit 10 letter words” but that’s just so I could analyze the process of the GA up to that point. It doesn’t add anything to the GA itself.

Also, despite the fact that I’m looking for any 10 letter word, there are only on the order of 10-20k of them (depending on how you count), and there are 26^10 or 1.4*10^14 possible 10-letter combinations, so the odds of picking any particular word of length 10 at random is still very low (about 1.4*10^-10).

If we’re just considering 8-bit single-byte coded graphic character sets I’d only find your results interesting if the generated word (or set of words) came to close to 500 informational bits.

I’m sorry you don’t find these results interesting . Making the assumptions I’ve used, do you know about how many letters would be equivalent to 500 informational bits?

http://user.tninet.se/~ecf599g.....index.html

http://www.uncommondescent.com/archives/1224

For fun see if your program can hit upon pseudopseudohypoparathyroidism (30 letters) or aequeosalinocalcalinoceraceoaluminosocupreovitriolic (52 letters). I’m glad to see your program doesn’t “sneak in too much information” considering the fitness function only checks for a 10-character string, although the target is very large considering you’re looking for ANY 10-letter word. If we’re just considering 8-bit single-byte coded graphic character sets I’d only find your results interesting if the generated word (or set of words) came to close to 500 informational bits.