Re: We believe in design

> David, I think we're talking at cross purposes here. I'm not
> talking about sub-optimal regions being eliminated quickly
> (presumably you mean by natural selection). I'm talking about the
> space of neutral mutations, where there is
> no selective advantage for one individual over any other in the same
> (sub)-space, because they all translate to the same phenotype. This
> is how I understood the paper cited by Pim.
>
> Do you agree that there can't be a selective advantage between two
> individuals that have different genotypes (due to neutrality), but
> the same phenotype?

Basically. There are caveats about possible variation due to non-
genetic factors, about at what level phenotype is determined (e.g.,
molecular differences versus gross morphology), and about how broadly
one considers advantages (does ease of evolving a useful non-neutral
difference from one allele of a neutral difference count as a
selective advantage?).
 
> If there can't be any selective advantage between members of this
> subset of genotype space, then the neutral mutations amount to
> nothing more than a random walk.

My point was that the subset of genotype space may be relatively well-
constrained, so that the random walk does a good job of reaching the
parts of interest. You are right that this does not affect the
efficency of the walk at reaching a given amount of space.

The computer example that I am most familiar with is the construction
of evolutionary trees based on large data sets of character states.
The goal is to either maximize probability or minimize some measure of
evolutionary change or distance across the entire tree-basically a
traveling salesman type of problem. Examination of the entire tree
space becomes unfeasible for more than about 11 species. However, an
exact solution can be determined for somewhat larger data sets (in my
experience, ca. 20-35 species) by elimination of clearly bad options.
As I'm often trying to deal with over 100 species, I have to use
approximate methods that nevertheless seem to give a pretty good
result. These work with some version of hill-climbing, i.e., from a
given tree they try various manipulations to look for a better tree.
This can get stuck in a local optimum rather than the absolute
optimum, though there are many tricks to try to improve things (e.g.,
examine some set of suboptimal trees, randomize the starting sequence
to try to sample more tree space).

This combination of random walks and selection seems fairly effective
at producing a reasonable solution to the problem.

-- 
Dr. David Campbell
425 Scientific Collections Building
Department of Biological Sciences
Biodiversity and Systematics
University of Alabama, Box 870345
Tuscaloosa AL 35487-0345  USA