>His lecture showed that any special search algorithm that is used to
>optimize a given quality (such as a fitness function) can on the
>average be no more efficient than a RANDOM search.<
I am currently running analyses that seek to optimize a given quality
(minimum parsimony) in an analysis of genetic data. The program uses
heuristic search algorithms that are far better than random searches.
What is the difference between this and the genetic algorithms?
Some possibilities that come to mind:
A search guarenteed to find the absolute optimum is much less
efficient than these heuristic approximations. However, living
organisms also get by just fine with suboptimal, but good, versions.
The heuristic analyses start with trying to find a good option and
then try to improve on it. Again, living organisms are generally
starting with a functional gene, and the number of changes needed to
make another functional gene is less than for starting from scratch.
Organisms have extra DNA. This means that they can have parallel
processing in searching for a particular result.
Not knowing the detailes of the programs and the precise goal of the
algorithms, I do not know how applicable any of these replies may be.
Dr. David Campbell
Old Seashells
University of Alabama
Biodiversity & Systematics
Dept. Biological Sciences
Box 870345
Tuscaloosa, AL 35487 USA
bivalve@mail.davidson.alumlink.com
That is Uncle Joe, taken in the masonic regalia of a Grand Exalted
Periwinkle of the Mystic Order of Whelks-P.G. Wodehouse, Romance at
Droitgate Spa
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