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 < nickthomp...@earthlink.net> 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 (nthomp...@clarku.edu) > http://home.earthlink.net/~nickthompson/naturaldesigns/ > http://www.cusf.org [City University of Santa Fe] > > > > >> [Original Message] >> From: John Kennison <jkenni...@clarku.edu> >> To: russ.abb...@gmail.com <russ.abb...@gmail.com>; The Friday > MorningApplied Complexity Coffee Group <friam@redfish.com> >> 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: friam-boun...@redfish.com [friam-boun...@redfish.com] On Behalf Of > Russ Abbott [russ.abb...@gmail.com] >> 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 > <teds...@gmail.com<mailto:teds...@gmail.com>> 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 > <e...@psu.edu<mailto:e...@psu.edu>> 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 > <russ.abb...@gmail.com<mailto: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> 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> 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 (nthomp...@clarku.edu) >> > http://home.earthlink.net/~nickthompson/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 >
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