🚀 go-pugleaf

andrew@rentec.com (Andrew Mullhaupt) writes:

>In article <1992Jul30.102118@trc.amoco.com> zkdc05@trc.amoco.com (Kelly D. Crawford) writes:
>>
>>GAs are more robust than SAs,

>This is as far as I can tell, a commonly accepted falsehood. You can transform
>any GA into SA by choosing a funny configuration space topology and annealing
>schedule. You can go the other way more transparently, (by multiplexing the
>search steps) so most people think that SA is properly included in GA, which I
>am certain is false. GA and SA solve the same class of problems, and the optimal
>GA for a problem is no better or no worse than the optimal SA for that problem.

I do not believe that you can make the transformations between GA's and
SA's that you indicate.  To transform the GA to an SA, you must first
note that the GA does not have an annealling aspect which forces it to
converge.  Any convergence in the GA is forced by the dynamics of
selection and cross-over.  Second, I assume that the 'funny
configuration space topology' would include the mutation and cross-over
points into the neighborhood used in SA.  Unfortunately, the
set of possible cross-over points is dependent on the current population.
There is no such dependency in SA.  In SA, the neighborhood structure
is fixed.

Performing the converse transformation is perhaps more understandable.
If you had a population of size one you wouldn't have cross-over and
would only have mutation steps, which is much like SA.  However, unless
you allow something like annealled mutation rates, you can't get a
transformation to work.

It is not clear to me that GA and SA solve the same class of problems.
Would you explain your reasoning?  The comparisons I have seen between
GA's and SA's have shown different relative performances on different
problems. 

--Bill