Re: [asa] Parameterized Evolution

On Aug 23, 2008, at 12:42 PM, Iain Strachan wrote:

>
>
> On Sat, Aug 23, 2008 at 5:18 PM, Rich Blinne <rich.blinne@gmail.com>
> wrote:
>
> On Aug 23, 2008, at 8:29 AM, Dave Wallace wrote:
>
> http://www.uncommondescent.com/biology/thoughts-on-parameterized-vs-open-ended-evolution-and-the-production-of-variability/
>
> I found this post on UcD somewhat interesting however, I have only a
> vague idea as to what Parameterized Evolution is. Can anyone point
> to a simple definition. As best I can tell it involves a
> predetermination/limitation of biological evolutionary search space.
> Dave W (ASA member)
>
> The reason why engineers are more prone to recognize this is because
> engineers have to develop systems repeatedly, and know how much
> trouble it is to get parts to play well together. Adjusting the
> system requires adjusting multiple parts simultaneously, which can't
> be accomplished without a guiding information system (which, in ID
> circles, is termed front-loaded evolution - which requires the
> action of an intelligent agent at the beginning) or the creativity
> and intervention of an intelligent agent at each step.
>
> This is such utter B.S. Speaking as an engineer, they don't have a
> clue on how it works. If you change everything simultaneously you
> get chaos. Rather, you make small revisions to working designs.
>
>
> I don't think it's complete B.S. - in fact I originally thought that
> your statement about changing everything simultaneously leading to
> chaos was also utter B.S., though I think I can see where you're
> coming from, and it's not the same place as the ID'er is coming
> from. I think ID folk tend to see evolution as a bit like the
> mathematical problem of trying to optimize an objective function of
> multiple variables, to find the correct combination. The final
> value of the objective function (which in a genetic algorithm would
> be the "fitness function") is dependent simultaneously on all the
> variables. Whether or not the problem can be solved by a genetic
> algorithm depends entirely on how tightly the variables are coupled
> together. For most of the problems I've worked on ( optimisation of
> weights in a neural network; optimisation of large chemical plants)
> the variables are always highly coupled. So the approach to take is
> to compute the gradient vector of the objective function with
> respect to all the variables that are to be tuned; then you make
> small steps in the direction of the gradient vector (or in a search
> direction based on the gradient vector, as in Conjugate Gradients or
> Quasi-Newton methods). In general most of the elements of the
> gradient vector are going to be non-zero, and hence you do indeed
> change everything simultaneously, and it does not lead to chaos, but
> allows the solution to be found in an iterative fashion. By
> contrast, if you vary one variable at a time, in turns, then you get
> an absolutely useless optimisation algorithm, unless the variables
> are decoupled. For example if you are trying to find the minimum of
> x^2 + y^2 + z^2 then the variables are decoupled, and you can change
> one at a time. But if you had xy + yz + zx then you could not.

One misconception of evolution is that it is an optimization
algorithm. Evolution by its nature hits "good enough" long before
optimum. That being said, hitting "good enough" is more like real-
world design than mathematical optimization. The real cost function is
not to produce a specific feature but survival of a genetic line in a
particular environment (which changes over time, more on this below).

>
>
> I think that is where the ID people are coming from; possibly
> influenced by misleading metaphors such as Dawkins's "Climbing Mount
> Improbable". I know this too, because I was misled by this analogy,
> and could easily see that such problems weren't easily soluble via a
> classical genetic algorithm. It was the main reason why I was
> attracted to ID originally - essentially the notion of "Irreducible
> Complexity" explained clearly to my why genetic algorithms (of the
> classical type of optimising parameter sets derived from mutating,
> and naturally selected bit strings), had so few examples where they
> worked. [ I now think whole "hill climbing" analogy is quite false -
> for a start the peak of the hill doesn't always stay in the same
> place!]

Let me expand on your last sentence. Again, because this is viewed as
an optimization algorithm what is not appreciated -- except by you --
is the so-called cost function is not static. What is being solved is
a nearby problem and thus amenable to genetic algorithms. That
solution is co-opted later on to solve a different problem. If
anything, the analogy is more of buying and adapting IP than building
from scratch. If the cost function did not change with time all
evolution would provide is mass extinction as you noted the difficulty
of a genetic algorithm to find solutions that have a high distance
from the original state. If you do need to change many variables
simultaneously it simply won't work and that was my original point.
Thus, you never get a beaver with a chain saw no matter how much
advantage such an adaptation would give. To be sure, if you see large
discontinuities as a result of many variable changes it would be a
coup de grace against evolution but that's not what the data shows.
There are no beavers with chain saws. Secular evolutionists -- despite
bad metaphors by Dawkins and company -- get this but ID proponents
have failed to understand this fairly simple concept over decades and
I find this quite frustrating.

>
>
> However, I think you are looking at it from a different
> perspective; you start with a good working design - one of the
> hallmarks of which is going to be modularity - it would be designed
> so that you could indeed change small bits without affecting the
> others. In such a case, you are indeed right in suggesting that
> changing everything at once would be chaotic.

Exactly. Evolution does show modularity in at least two ways:

1. Many evolutionary conserved functions.
2. So-called convergent evolution where similar functions are derived
through different descent lines.

This shows that a goal or design is quite compatible with the
evolutionary process. IDM needs to spend more time trying to
understand the evolutionary process than falsely claiming superiority.
Then they might have more chance at success with their original goal
of providing evidence that creation and specifically life is designed
by God.

Rich Blinne
Member ASA

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Received on Sat Aug 23 15:37:42 2008