Quote ---> " My mother will be comforted to learn this has been confirmed by experiments on chickens."
LOL. That's the funniest thing I've read all week. -t On Sat, Jul 10, 2010 at 11:38 PM, Russ Abbott <[email protected]> wrote: > Exactly. Although this was not (as far as I know) part of the experiment, > one could imagine a similar experiment on groups with more structure, e.g., > baseball teams. It's the team that wins the most games (or the most > important games) that reproduces. That team probably has pretty good players > at each position, but almost certainly it has a good team structure and team > organization. In other words, they work well together. That's what matters. > > > -- Russ Abbott > ______________________________________ > > Professor, Computer Science > California State University, Los Angeles > > cell: 310-621-3805 > blog: http://russabbott.blogspot.com/ > vita: http://sites.google.com/site/russabbott/ > ______________________________________ > > > > On Sat, Jul 10, 2010 at 8:17 PM, Nicholas Thompson < > [email protected]> wrote: > >> Perhaps if I understood the computer side of this conversation better I >> wouldn't have the feeling that the chicken example is being misunderstood. >> But I dont and I do (respectively). It should be remembered that no >> chickens were selected during the conduct of this experiment; only >> crates. What determined if crates were allowed to contribute to the next >> generation was the number of eggs that the crate laid. >> >> Chickens changed, but selection was for crate egg production. Changed >> chicken behavior mediated the change in crate reproductive output. >> >> Eliot Sober makes an interesting distinction between selection of and >> selection for. The experiment resulted in the selction of nice chickens, >> but selection was for crate egg production. >> >> N >> >> Nicholas S. Thompson >> Emeritus Professor of Psychology and Ethology, >> Clark University ([email protected]) >> http://home.earthlink.net/~nickthompson/naturaldesigns/<http://home.earthlink.net/%7Enickthompson/naturaldesigns/> >> http://www.cusf.org [City University of Santa Fe] >> >> >> >> >> >> ----- Original Message ----- >> *From:* sarbajit roy <[email protected]> >> *To: *[email protected];The Friday Morning Applied Complexity >> Coffee Group <[email protected]> >> *Sent:* 7/10/2010 10:51:22 PM >> *Subject:* Re: [FRIAM] Real-world genetic algorithm example... help! >> >> How is selective breeding / clustering to optimise particular traits in >> chickens any different from endogamous human clusters / societies? In India >> for eg. the endgamous caste and sub-caste systems have been in place for >> millenia to ensure genetic optimisation and perpetuation of a few >> "desirable" traits. My mother will be comforted to learn this has been >> confirmed by experiments on chickens. Previously all Bengali Brahmins had to >> rely on were encyclopedias / papers like this >> [1<http://en.wikipedia.org/wiki/Haplogroup_R1a1_%28Y-DNA%29>] >> to confirm that "we" are bred to perpetuate an "R1a1" gene. <rol> >> >> Sarbajit >> >> On Sat, Jul 10, 2010 at 8:31 PM, Nicholas Thompson < >> [email protected]> wrote: >> > John, >> > >> > Thanks. I agree. In fact, I would argue that ANY attempt to squeeze >> > spiritual juice from this particular example blunts it scientific edge. >> > >> > To mix a metaphor. >> > >> > N >> > >> > Nicholas S. Thompson >> > Emeritus Professor of Psychology and Ethology, >> > Clark University ([email protected]) >> > http://home.earthlink.net/~nickthompson/naturaldesigns/<http://home.earthlink.net/%7Enickthompson/naturaldesigns/> >> > http://www.cusf.org [City University of Santa Fe] >> > >> > >> > >> > >> >> [Original Message] >> >> From: John Kennison <[email protected]> >> >> To: [email protected] <[email protected]>; The Friday >> > MorningApplied Complexity Coffee Group <[email protected]> >> >> Date: 7/10/2010 4:02:16 AM >> >> Subject: Re: [FRIAM] Real-world genetic algorithm example... help! >> >> >> >> Selecting for productive coops rather than productive hens might reject >> > highly productive, highly aggressive hens in favor of somewhat less >> > productive, considerably less aggressive hens who would leave their >> > coop-mates in peace (and therefore able to produce more eggs). Such hens >> > need not have anything like a “concept” of loyalty to the coop --we >> could >> > redistribute these hens to different coops and without affecting coop >> > productivity. But after a while, we might find we are selecting for >> highly >> > productive, potentially highly aggressive hens who are strongly >> inhibited >> > against bothering a hen they grew up with. Then if we redistributed the >> > hens among different coops, coop productivity would decrease. >> >> >> >> Are there applications to genetic algorithms? It shows you have to be >> > careful about dividing the task to be done into subtasks. You don’t want >> to >> > overlook an algorithm for doing one subtask that provides useful >> byproducts >> > for another subtask. Instead of selecting for each subtask separately, >> you >> > might select for teams of algorithms that do the whole task. >> >> >> >> ________________________________________ >> >> From: [email protected] [[email protected]] On Behalf >> Of >> > Russ Abbott [[email protected]] >> >> Sent: Saturday, July 10, 2010 2:50 AM >> >> To: The Friday Morning Applied Complexity Coffee Group >> >> Subject: Re: [FRIAM] Real-world genetic algorithm example... help! >> >> >> >> It's not a good example as an illustration of GA because (1) the >> > "selection" mechanism to move from one generation to the next is >> > essentially select the best and shake it up. At best you might call that >> > elitism plus mutation. But it is not representative of GA. (2) it has no >> > explicit representation of the genome (3) there are no explicit genetic >> > operators applied to one or more parents to produce children. >> >> >> >> The issue of whether there is mutation points out that there is no coop >> > genome that is being evolved. Since there is no coop genome, it's hard >> to >> > say that there is or is not mutation. It certainly isn't a good >> > illustration of mutation for a textbook. >> >> >> >> You might make the case that the coop genome is the collection of the >> > chicken genomes and that the offspring coop genome is generated from the >> > parent coop genome by breeding the chickens. I guess you could call that >> > mutation of the coop genome.So the mutation operator on the parent coop >> > genome is to breed the chickens to get a new coop genome. But I think >> > that's about as far as you could push it. >> >> >> >> If I were forced to describe this in GA terms, I would say that the >> coop >> > genome is the sequence, in some arbitrary order, of chicken genomes. To >> get >> > an offspring, take a coop genome and treat the segments that correspond >> to >> > individual chickens as separate genomes, mate them to get offspring, and >> > then concatenate the genomes of the resulting offspring to get a new >> coop >> > genome. I've never heard of a genetic operator like that, but I guess >> that >> > doesn't mean you couldn't claim it as a genetic operator. >> >> >> >> The bottom line for me though is that the experiment is great biology, >> > but it's a pretty limited and confusing example of a GA. >> >> >> >> >> >> -- Russ Abbott >> >> ______________________________________ >> >> >> >> Professor, Computer Science >> >> California State University, Los Angeles >> >> >> >> cell: 310-621-3805 >> >> blog: http://russabbott.blogspot.com/ >> >> vita: http://sites.google.com/site/russabbott/ >> >> ______________________________________ >> >> >> >> >> >> >> >> On Fri, Jul 9, 2010 at 11:12 PM, Ted Carmichael >> > <[email protected]<mailto:[email protected]>> wrote: >> >> Ha! I knew someone would complain about that. >> >> >> >> First of all, Eric is correct: the main point of the story - beyond a >> > nice, illustrative example of how a GA works - is the need to properly >> > define a fitness function. In the case of individual chickens, the >> fitness >> > function was ill-defined and didn't work very well. In particular, this >> > section points out that it is not necessary to know why a good solution >> is >> > good. Why doesn't have to come into it ... the fitness function simply >> > ensures that the best solution, no matter what the reasons are for being >> > the best, can emerge from this process. >> >> >> >> In regards to Russ' complaints, I'm not sure I can agree that no >> > crossover/mutation occurred. I haven't read the original paper yet, >> just >> > the Huff Post treatment, so I didn't realize that the chicken clusters >> > weren't mixed. That is, I just assumed that more than one cluster was >> > selected among the best, and that they collectively produced the >> subsequent >> > generations. >> >> >> >> However, consider the case of mutation. Russ says there is no mutation >> > within the population elements - the clusters of chickens. But >> > functionally, there actually is mutation. This becomes obvious when we >> > remember that a second-generation chicken coop is different from the >> > first-generation coop. The genes were all there, but some of them >> weren't >> > expressed ... that is, they simply combined together in a different way >> to >> > produce a different coop. It doesn't matter that the kids have all the >> > genes of the parents ... the kids are still different. >> >> >> >> And we know this is true because egg production went up. This couldn't >> > have happened unless there was something (crossover or mutation) that >> > changed from generation to generation. >> >> >> >> Regarding James' point, I don't know how the roosters were handled from >> > generation to generation (something that is probably in the original >> > paper). But I suppose they could get the next generation roosters the >> same >> > way they got the next generation hens - by simply hatching a few eggs. >> >> >> >> One final point: since GA originally got its inspiration from biology, >> I >> > see no reason why biology can be used to illustrate GA in a textbook. >> > Thoughts? >> >> >> >> Cheers, >> >> >> >> -Ted >> >> >> >> On Fri, Jul 9, 2010 at 10:03 PM, ERIC P. CHARLES >> > <[email protected]<mailto:[email protected]>> wrote: >> >> Russ, >> >> Completely agreed. >> >> I'm not sure how one would connect the chicken stuff in a pretty way to >> > standard computer genetic algorithms. I suppose one could relate them >> > together to suggest the need for variation in "selection" methods when >> > using GAs. That's Ted's part. I only claimed to know how the chicken >> part >> > worked through (either artificial or natural) selection for something >> other >> > than best individual production. >> >> >> >> Eric >> >> >> >> >> >> On Fri, Jul 9, 2010 09:18 PM, Russ Abbott >> > <[email protected]<mailto:[email protected]>> 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 <[email protected]> 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 <[email protected]> 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 <[email protected]> >> 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 >> > <[email protected]> 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_Tx >> > t_Recherche_name_attr=auteursNom:%20%28MUIR%29> ; >> >> Revue / Journal Title >> >> Poultry >> > science< >> http://www.refdoc.fr/?traduire=en&FormRechercher=submit&FormRecherch >> > er_Txt_Recherche_name_attr=listeTitreSerie:%20%28Poultry%20science%29> >> > ISSN >> > 0032-5791< >> http://www.refdoc.fr/?traduire=en&FormRechercher=submit&FormRecher >> > cher_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 ([email protected]) >> >> >> > http://home.earthlink.net/~nickthompson/naturaldesigns/<http://home.earthlink.net/%7Enickthompson/naturaldesigns/> >> <http://home.earthlin >> > k.net/%7Enickthompson/naturaldesigns/> >> >> http://www.cusf.org [City University of Santa Fe] >> >> >> >> >> >> >> >> >> >> ----- Original Message ----- >> >> From: Ted Carmichael >> >> To: The Friday Morning Applied Complexity Coffee Group >> >> 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 >> >> >> >> >> >> 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 >> >> >> >> >> >> ============================================================ >> >> 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 >> > > > ============================================================ > 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 >
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