Well, it wouldn't ... unless you were selecting for the lowest
producing hens.
The GA selects for the /groups /of chickens that produce the most
eggs, not the individuals. Some of those individuals may actually
not produce many eggs, but they must somehow help the ones that do
produce more eggs (in their group).
-t
On Fri, Jul 9, 2010 at 6:47 PM, Shawn Barr <sba...@gmail.com
<mailto:sba...@gmail.com>> wrote:
Ted,
I'm confused. Why would a genetic algorithm ever select a hen
that produces fewer eggs over a hen that produces more eggs?
Shawn
On Fri, Jul 9, 2010 at 2:57 PM, Ted Carmichael <teds...@gmail.com
<mailto:teds...@gmail.com>> wrote:
Nick, this is perfect. Thank you!
BTW - the reason for this request is, my advisor and I were
asked to write a chapter on Complex Adaptive Systems, for a
cognitive science textbook. In it, I talk briefly about GA,
and put this story about the chickens in because I thought it
was a neat example.
I'll add the references now. Much appreciated.
-t
On Fri, Jul 9, 2010 at 12:28 PM, Nicholas Thompson
<nickthomp...@earthlink.net
<mailto:nickthomp...@earthlink.net>> wrote:
Ted,
Ok. So, if I am correct, this was an actual EXPERIMENT
done by two researchers at Indiana University, I think.
As I "tell" the "story", it was the practice to use
individual selection to identify the most productive
chickens, but the egg production method involved crates
of nine chickens. The individual selection method
inadvertently selected for the most aggressive chickens,
so that once you threw them together in crates of nine,
it would be like asking nine prom queens to work together
in a tug of war. The chickens had to be debeaked or they
would kill each other. So, the researchers started
selection for the best producing CRATES of chickens.
Aggression went down, mortality went down, crate
production went up, and debeaking became unnecessary.
The experiment is described in Sober and Wilson's UNTO
OTHERS or Wilson's EVOLUTION FOR EVERYBODY, which are
safely tucked away in my book case 2000 miles away in
Santa Fe. Fortunately, it is also described in
Dave Wilson's blog
http://www.huffingtonpost.com/david-sloan-wilson/truth-and-reconciliation_b_266316.html
Here is the original reference:
GROUP SELECTION FOR ADAPTATION TO MULTIPLE-HEN CAGES :
SELECTION PROGRAM AND DIRECT RESPONSES
Auteur(s) / Author(s)
MUIR W. M.
<http://www.refdoc.fr/?traduire=en&FormRechercher=submit&FormRechercher_Txt_Recherche_name_attr=auteursNom:%20%28MUIR%29>
;
Revue / Journal Title
Poultry science
<http://www.refdoc.fr/?traduire=en&FormRechercher=submit&FormRechercher_Txt_Recherche_name_attr=listeTitreSerie:%20%28Poultry%20science%29>
*ISSN* 0032-5791
<http://www.refdoc.fr/?traduire=en&FormRechercher=submit&FormRechercher_Txt_Recherche_name_attr=identifiantsDoc:%20%280032-5791%29>
*CODEN* POSCAL
Source / Source
1996, vol. 75, n^o 4, pp. 447-458 [12 page(s) (article)]
If you Google "group selection in chickens," you will
find lots of other interesting stuff.
Let me know if this helps and what you think.
N
Nicholas S. Thompson
Emeritus Professor of Psychology and Ethology,
Clark University (nthomp...@clarku.edu
<mailto:nthomp...@clarku.edu>)
http://home.earthlink.net/~nickthompson/naturaldesigns/
<http://home.earthlink.net/%7Enickthompson/naturaldesigns/>
http://www.cusf.org [City University of Santa Fe]
----- Original Message -----
*From:* Ted Carmichael <mailto:teds...@gmail.com>
*To: *The Friday Morning Applied Complexity Coffee
Group <mailto:friam@redfish.com>
*Sent:* 7/9/2010 5:34:29 AM
*Subject:* [FRIAM] Real-world genetic algorithm
example... help!
Dear all,
I'm trying to find reference to a story I read some
time ago (a few years, perhaps?), and I'm hoping that
either: a) I heard it from someone on this list, or
b) someone on this list heard it, too.
Anyway, it was a really cool example of a real-world
genetic algorithm, having to do with chickens.
Traditionally, the best egg-producing chickens were
allowed to produce the offspring for future
generations. However, these new chickens rarely
lived up to their potential. It was thought that
maybe there were unknown things going on in the
/clusters /of chickens, which represent the actual
environment that these chickens are kept in. And
that the high producers, when gathered together in
these groups, somehow failed to produce as many eggs
as expected.
So researchers decided to apply the fitness function
to /groups /of chickens, rather than individuals.
This would perhaps account for social traits that
are generally unknown, but may affect how many eggs
were laid. In fact, the researchers didn't care what
those traits are, only that - whatever they may be -
they are preserved in future generations in a way
that increased production.
And the experiment worked. Groups of chickens that
produced the most eggs were preserved, and subsequent
generations were much more productive than with the
traditional methods.
Anyway, that's the story. If anyone can provide a
link, I would be very grateful. (As I recall, it
wasn't a technical paper, but rather a story in a
more accessible venue. Perhaps the NY Times article,
or something similar?)
Thanks!
-Ted
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FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
