I should also have added that unlike GAs in which one is manipulating an explicit genome, there was no explicit genome in this experiment.
Russ On Fri, Jul 9, 2010 at 6:18 PM, Russ Abbott <russ.abb...@gmail.com> wrote: > It's a great story, but it's not a genetic algorithm as we normally think > about it. It's really just breeding. For one thing, no computer was > involved. The point of the whole thing is to establish the notion of group > selection, which was forbidden in the biological world for a while. This > experiment shows that it makes sense. > > In what sense was it just breeding? Well, what was bred was coops rather > than chickens. So the original population was 6 coops. The best one was > selected and propagated. The best of those was selected, etc. Not at all > what GA is about. There was no crossover or mutation between the population > elements -- which are coops. Of course there is crossover among the > chickens in the coop, but it wasn't chickens that were bred. The fitness > function was a function applied to the coop. > > So even though it is a very nice experiment and even though it makes a very > strong case for group selection, it's probably not a good example for a > chapter on genetic algorithms in a text book. > > > -- Russ > > > > On Fri, Jul 9, 2010 at 4:25 PM, ERIC P. CHARLES <e...@psu.edu> wrote: > >> Shawn, >> The two ways to answer your question would either be to invoke artificial >> selection (i.e., because you can design a genetic algorithm to do anything >> you want, just as chicken breeders can keep whichever eggs or to invoke >> Wilson's "trait group selection." In trait group selection you break >> selection into two parts, within-group and between-group selection. If you >> do that, you can, under the right conditions, find that types of individuals >> who reproduce less well within any group can still out-compete the >> competition when you look between groups. Math available upon request. I >> have a vague memory that this has come across the FRIAM list before. >> >> Eric >> >> >> On Fri, Jul 9, 2010 06:47 PM, *Shawn Barr <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<#129b9eeb2de0c15f_129b987e5d851537_> >> > 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 <#129b9eeb2de0c15f_129b987e5d851537_>> 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, no4, 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<#129b9eeb2de0c15f_129b987e5d851537_> >>>> ) >>>> 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 <#129b9eeb2de0c15f_129b987e5d851537_> >>>> *To: *The Friday Morning Applied Complexity Coffee >>>> Group<#129b9eeb2de0c15f_129b987e5d851537_> >>>> *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 >>>> >>>> >>>> ============================================================ >>>> 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 >> >> Eric Charles >> >> Professional Student and >> Assistant Professor of Psychology >> Penn State University >> Altoona, PA 16601 >> >> >> >> ============================================================ >> 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
