4 ms·

Again and irrespective of how much genome information was there initially and what it eventually became, you are still talking about a final optimization proble

Again and irrespective of how much genome information was there initially and what it eventually became, you are still talking about a final optimization problem of 4^1,000,000,000. Even one tenth of that amount of the human genome is an unfathomably large number to randomly iterate to given the generally accepted statistics cited above. The math behind stochastic molecular Darwinism doesn't work out at all.

fourthark 7y ago
I don't see where you are getting the idea that humans had to be pulled out of a hat of all possible genetic sequences. They, like, evolved, right? As the GP says, there was a short sequence that worked, a little got built on, a little more... There was never any time that any creature was generated by random choice.
- GregoryPerry 7y ago
  Got math?
  - GregoryPerry 7y ago
    What in the world are you talking about? This thread of discussion is about the computationally intractable nature of 4^1,000,000,000 Got math? Maybe post a proof?
    - fourthark 7y ago
      Tell me why evolution would require all of those combinations to be tried? Edit: Microsoft Windows 10 is 9GB. It would be impossible to try 8^9000000000 different programs. Yet, Windows exists, and most of us believe it's contained in those 9GB.
      - GregoryPerry 7y ago
        So per your logic the Windows 10 operating system was created by random iteration of x86 opcodes over a lengthy period of time? Huh?
        fourthark 7y ago
        Exactly the opposite. Just because there are so many possibilities doesn't mean that all of them have to be tried or make sense. You wouldn't code that way and nature doesn't either.
    - Dylan16807 7y ago
      If you just want to talk about how computationally tractable it is, the math is trivial. Optimize one base pair at a time. Now it's an O(4) problem repeated over a billion generations, most of which are bacteria where a generation is measured in minutes. In practice the changes happening in each generation are all sorts of different rearrangements, but that's different from proving the basic and obvious fact that when you have multiple steps you don't have to spontaneously create the entire solution at once. Bogosort will never ever sort a deck of cards. Yet it takes mere minutes to sort a deck of cards with only the most basic of greater/less comparisons. Even if your comparisons are randomized, and only give you the right answer 60% of the time, you can still end up with a sorted-enough deck quite rapidly. (Why sorted-enough? Remember that reaching 'human' doesn't require any exact setup of genes, every single person has a different genome. It just has to get into a certain range.)
nootropicat 7y ago
There's no random iteration, it's more like stochastic gradient descent with noise. Your number isn't correct even if only because of codon degeneracy.
- GregoryPerry 7y ago
  Haha, so biological neuronal processes utilize a method of gradient descent? Perhaps you should submit your findings to the Nobel Prize Committee :) Again, this thread is about the computationally intractable nature of 4^1,000,000,000. Got math? A proof maybe to support your statements?
  - gus_massa 7y ago
    >>> you are still talking about a final optimization problem of 4^1,000,000,000. There is no final optimization step that analyze the 4^1,000,000,000 possibilities. We are not the best possible human-like creature with 1,000,000,000 pairs of bases. > method of gradient descent Do you know the method of gradient descent? Nice. It is easier to explain the problem if you know it. In the method of gradient descent you don't analyze all the possible configurations and there is no guaranty that it finds the absolute minimum. It usually finds a local minimum and you get trapped there. For this method you need to calculate the derivatives, analytically or numerically. And looking at the derivatives at a initial point, you select the direction to move for the next iteration. An alternative method is to pick e few (10? 100?) random points nearby your initial point, calculate the function in each of them and select the one with the minimum value for the next iteration. It's not as efficient as method of gradient descent, but just by chance half of the random points should get a smaller value (unless you are to close to the minimum, or the function has something strange.) So just this randomized method should find also the "nearest" local minimum. The problem with the DNA is that it is a discrete problem, and the function is weird, a small change can be fatal of irrelevant. So it has no smooth function where you can apply the method of gradient descent, but you can still try picking random points and selecting one with a smaller value. There is no simulation that picks the random points and calculate the fitness function. The real process in the offspring, the copies of the DNA have mutations and some mutations made kill the individual, some make nothing and some increase the chance to survive and reproduce.

fourthark 7y ago

I don't see where you are getting the idea that humans had to be pulled out of a hat of all possible genetic sequences. They, like, evolved, right? As the GP says, there was a short sequence that worked, a little got built on, a little more... There was never any time that any creature was generated by random choice.

GregoryPerry 7y ago
Got math?
- GregoryPerry 7y ago
  What in the world are you talking about? This thread of discussion is about the computationally intractable nature of 4^1,000,000,000 Got math? Maybe post a proof?
  - fourthark 7y ago
    Tell me why evolution would require all of those combinations to be tried? Edit: Microsoft Windows 10 is 9GB. It would be impossible to try 8^9000000000 different programs. Yet, Windows exists, and most of us believe it's contained in those 9GB.
    - GregoryPerry 7y ago
      So per your logic the Windows 10 operating system was created by random iteration of x86 opcodes over a lengthy period of time? Huh?
      - fourthark 7y ago
        Exactly the opposite. Just because there are so many possibilities doesn't mean that all of them have to be tried or make sense. You wouldn't code that way and nature doesn't either.
  - Dylan16807 7y ago
    If you just want to talk about how computationally tractable it is, the math is trivial. Optimize one base pair at a time. Now it's an O(4) problem repeated over a billion generations, most of which are bacteria where a generation is measured in minutes. In practice the changes happening in each generation are all sorts of different rearrangements, but that's different from proving the basic and obvious fact that when you have multiple steps you don't have to spontaneously create the entire solution at once. Bogosort will never ever sort a deck of cards. Yet it takes mere minutes to sort a deck of cards with only the most basic of greater/less comparisons. Even if your comparisons are randomized, and only give you the right answer 60% of the time, you can still end up with a sorted-enough deck quite rapidly. (Why sorted-enough? Remember that reaching 'human' doesn't require any exact setup of genes, every single person has a different genome. It just has to get into a certain range.)

nootropicat 7y ago

There's no random iteration, it's more like stochastic gradient descent with noise. Your number isn't correct even if only because of codon degeneracy.

GregoryPerry 7y ago
Haha, so biological neuronal processes utilize a method of gradient descent? Perhaps you should submit your findings to the Nobel Prize Committee :) Again, this thread is about the computationally intractable nature of 4^1,000,000,000. Got math? A proof maybe to support your statements?
- gus_massa 7y ago
  >>> you are still talking about a final optimization problem of 4^1,000,000,000. There is no final optimization step that analyze the 4^1,000,000,000 possibilities. We are not the best possible human-like creature with 1,000,000,000 pairs of bases. > method of gradient descent Do you know the method of gradient descent? Nice. It is easier to explain the problem if you know it. In the method of gradient descent you don't analyze all the possible configurations and there is no guaranty that it finds the absolute minimum. It usually finds a local minimum and you get trapped there. For this method you need to calculate the derivatives, analytically or numerically. And looking at the derivatives at a initial point, you select the direction to move for the next iteration. An alternative method is to pick e few (10? 100?) random points nearby your initial point, calculate the function in each of them and select the one with the minimum value for the next iteration. It's not as efficient as method of gradient descent, but just by chance half of the random points should get a smaller value (unless you are to close to the minimum, or the function has something strange.) So just this randomized method should find also the "nearest" local minimum. The problem with the DNA is that it is a discrete problem, and the function is weird, a small change can be fatal of irrelevant. So it has no smooth function where you can apply the method of gradient descent, but you can still try picking random points and selecting one with a smaller value. There is no simulation that picks the random points and calculate the fitness function. The real process in the offspring, the copies of the DNA have mutations and some mutations made kill the individual, some make nothing and some increase the chance to survive and reproduce.