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://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 dont 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.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
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