NB, in a computer GA, we'd just mix-n-match the hens genes. In the real world, you can't get more hens without roosters, or a least without their "input", and there's no mixing of between the hens at all. ~~James
On 7/9/10, Russ Abbott <russ.abb...@gmail.com> wrote: > 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
