Real-world genetic algorithm example... help!
Locked
12
12
Re: Real-world genetic algorithm example... help!
 
|
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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]>* 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 >>> <[hidden email]<#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 < >>>> [hidden email] <#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 >>>>> ([hidden email]<#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 |
|
|
In reply to this post by QEF@aol.com
Dunno. Not familiar with that. One aim of mine with this book is to phrase these ideas in a way that the beloved General Public can use them. Not just B-school types. I want the basic concept to be generally accessible. Needs to be, after all.
Will look into this. Has it affected how you conceptualize and take action on ideas and goals? Or was it interesting (partly because of the alliteration, that memorable lilting he set up sticks in our brains like the Oscar Mayer Weiner song) Happy to hear speculations, no worries. Tory On Jul 9, 2010, at 6:40 PM, [hidden email] wrote: Tory -- ----------------------------------- TORY HUGHES Tory Hughes website ------------------------------------ ============================================================ 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 |
Algorithm of chickens and the Swiss Army
|
|
In reply to this post by Robert J. Cordingley
Not all of us are run by our lizard brains. Speak for yourself, white male person. I haven't gone to war with nobody, despite much provocation.
Seriously, check out Don Beck and SDI for a useful system that places tribal values in a developmental continuum: we do in fact outgrow the crate 'chicken mentality. For example Switzerland has the ability and the experience historically to be quite aggressive. Yet they converted their resources into impressive defenses, and becoming a culture that the Wealthy+ Powerful prefer remain stable; so the W+P can safeguard all that lovely money from the weapons they sold to other chickens. For that, check out John McPhee's wonderful "La Place de la Concorde Suisse" about the Swiss army and the transformation of the primal need to be safe from an aggressively warring model to an aggressively defended model. Many other examples, interesting opportunity to compare and contrast. But that means writing. Back to my book. Been a pleasure dipping into the discussion for a few hours. Tory On Jul 9, 2010, at 7:30 PM, Robert J. Cordingley wrote:
----------------------------------- TORY HUGHES Tory Hughes website ------------------------------------ ============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Eric Charles
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 <[hidden email]> wrote:
============================================================ 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 |
Re: 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 <[hidden email]> wrote: Ha! I knew someone would complain about that. ============================================================ 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 |
Re: 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: [hidden email] [[hidden email]] On Behalf Of Russ Abbott [[hidden email]] 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 <[hidden email]<mailto:[hidden email]>> 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 <[hidden email]<mailto:[hidden email]>> 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 <[hidden email]<mailto:[hidden email]>> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 ([hidden email]) 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 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Russ Abbott
Well, in regards to (1), yes, I would guess elitism + mutation is a good description. However, I believe that is enough to qualify as a GA. As I recall, some GA practitioners believe mutation is best, some believe crossover is best, and some feel you should have both, or decide based on the problem. I would guess that it is a minority viewpoint to claim that mutation by itself is not enough to be a GA.
For (3), you say that the genetic operators are not explicit, in the chicken coop example. My understanding is that sometimes the genetic operator itself is a part of the genotype, and is thus subject to mutation/crossover as well. In such a case, it wouldn't really be explicit because it would vary across the population. Of course, the final genetic operator is discoverable - i.e., it is recorded in the final solution ... but this doesn't really matter to the programmer. He has no knowledge a priori of what that operator will turn out to be; and further, the final genetic operator is not the point of the GA. Finding a good solution is the desired outcome - the operator is secondary at best.
For (2), you correctly imply that a phenotype - or genotype - must be explicitly defined in a computer. Well, sure. The computer is deterministic, and so this information will be recorded somewhere as part of the code of the solution. What I find interesting is this idea that the programmer has to know and care and understand the final solution. I don't think that is the case.
Oh sure, in some instances the final solution is quite clear. For example, the TSP will end up with a list of city-pairs that is easily understood. But I can also imagine instances of a GA, say applied to computer code, or a mathematical formula, that becomes so immensely complex that the researcher does not understand why the final solution works. And, as pointed out above, he doesn't have to understand why it works. That it does work well is good enough, isn't it?
I just don't see any functional distinction between not caring why the final solution works and - in the case of chickens - not being able to precisely describe how it works or what it looks like. It's kind of like that DARPA funded robot pack-animal ... they don't care, really, what all the final rules are, as long as the robot can walk. In fact, I would suspect that some flexibility in the 'final solution' is allowed, and that the machine learning process is continuously running to some degree.
But I am enjoying the discussion, so thanks for that. Cheers, -Ted
On Sat, Jul 10, 2010 at 2:50 AM, Russ Abbott <[hidden email]> wrote:
============================================================ 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 |
|
|
In reply to this post by Victoria Hughes
Tory --
It was mostly that the stages seem to be empirically valid - I can recall many instances where I've been in a team or relationship that had the excitement and novelty of coming together, the inevitable misunderstandings/arguments about how to proceed, a reconciliation and synthesis of the preferred approach, and finally, working together along those preferred lines to achieve something. The hazard is that it's presented in such a way as to suggest that there's an orderly, linear progression, whereas we know it's often quite the contrary. I tend to see it (and, relating it to my thinking) as a continuum - the progression is a road map, perhaps, but I'm likely to be taking on ramps and off ramps along the way. It's not at all clear, either in my own mind or when I'm working with others, where the transitions occur: no bright line between "storming" and "norming", for example. - Claiborne - -----Original Message-----
From: Victoria Hughes <[hidden email]> To: The Friday Morning Applied Complexity Coffee Group <[hidden email]> Sent: Fri, Jul 9, 2010 11:00 pm Subject: Re: [FRIAM] Projects: 5 Stages
Dunno. Not familiar with that. One aim of mine with this book is to phrase these ideas in a way that the beloved General Public can use them. Not just B-school types. I want the basic concept to be generally accessible. Needs to be, after all.
Will look into this.
Has it affected how you conceptualize and take action on ideas and goals?
Or was it interesting (partly because of the alliteration, that memorable lilting he set up sticks in our brains like the Oscar Mayer Weiner song)
Happy to hear speculations, no worries.
Tory
On Jul 9, 2010, at 6:40 PM, [hidden email] wrote:
Tory -- -----------------------------------
TORY HUGHES Tory Hughes website
------------------------------------
============================================================ ============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Ted Carmichael
Everybody,
Why do the best conversations happen when I am totally unable to pay proper attention to them!?
Somebody help me out here. A genetic algorithm is a PROCEDURE, right? So you run the procedure on a computer. Is it possible to implement that same procedure on crates of chickens. In Gallinacea so to speak? Let the group of chickens compute the algorithm? Total misuse of language?
Take groups of chickens, raise them in crates of nine. The crate is the "individual"; the individual chickens are the "germ cells". Count the number of eggs produced by the crates. Hatch the eggs produced by the crate with the most eggs. Raise the chicklets. Put them in crates of nine. Etc.
Hot as hell here and hard to THINK.
n
Nicholas S. Thompson
Emeritus Professor of Psychology and Ethology,
Clark University ([hidden email])
http://www.cusf.org [City University of Santa Fe]
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Ted Carmichael
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 ([hidden email]) http://home.earthlink.net/~nickthompson/naturaldesigns/ http://www.cusf.org [City University of Santa Fe] > [Original Message] > From: John Kennison <[hidden email]> > To: [hidden email] <[hidden email]>; The Friday MorningApplied Complexity Coffee Group <[hidden email]> > 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: [hidden email] [[hidden email]] On Behalf Of Russ Abbott [[hidden email]] > 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 > Ha! I knew someone would complain about that. > > First of all, Eric is correct: the main point of the story - beyond a 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 <[hidden email]<mailto:[hidden email]>> 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 <[hidden email]<mailto:[hidden email]>> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 ([hidden email]) > > 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 > > 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Ted Carmichael
I have been here before. This is the point in the conversation where Roger Critchlow explains to me what the hell is going or .... or, i die. Roger?
Is there a confusion here concerning what is the analogue of the individual in the genetic algorithm?
Nick
Nicholas S. Thompson
Emeritus Professor of Psychology and Ethology,
Clark University ([hidden email])
http://www.cusf.org [City University of Santa Fe]
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Nick Thompson
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] to confirm that "we" are bred to perpetuate an "R1a1" gene. <rol>
Sarbajit On Sat, Jul 10, 2010 at 8:31 PM, Nicholas Thompson <[hidden email]> 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 ([hidden email]) > http://home.earthlink.net/~nickthompson/naturaldesigns/ > http://www.cusf.org [City University of Santa Fe] > > > > >> [Original Message] >> From: John Kennison <[hidden email]> >> To: [hidden email] <[hidden email]>; The Friday > MorningApplied Complexity Coffee Group <[hidden email]> >> 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: [hidden email] [[hidden email]] On Behalf Of > Russ Abbott [[hidden email]] >> 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 > <[hidden email]<mailto:[hidden email]>> 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 > <[hidden email]<mailto:[hidden email]>> 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 > <[hidden email]<mailto:[hidden email]>> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 > <[hidden email]> 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 ([hidden email]) >> > 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 > ============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Ted Carmichael
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 ([hidden email])
http://www.cusf.org [City University of Santa Fe]
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
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 <[hidden email]> wrote:
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
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 <[hidden email]> wrote:
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Ted Carmichael
the deep paradox is how do you select for individuals that make good groups. Persumably the best groups integrate many talents, but individual selection tends to pull for slight variations amongs the "best" individual. In many cases, group selection pulls for adaptible individuals .. think about group selection in the immune system,for instance. I think this might explain why human children seem to come prepackaged with their own unique talents, etc., that seem to come out of the blue. Viewed in that light, things like asbergers suddenlybegin to seem like talents that might come in useful in a group.
n
Nicholas S. Thompson
Emeritus Professor of Psychology and Ethology,
Clark University ([hidden email])
http://www.cusf.org [City University of Santa Fe]
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
Another way to put it is that group members can be capable of performing any the group tasks but perform the one that is appropriate given the circumstances they find themselves in. Bees in a colony etc. all have the same genome, but they act differently depending on their circumstances. In fact, the same thing is true of our cells. All the same genome but expressed differently under different circumstances.
-- Russ On Sun, Jul 11, 2010 at 10:18 AM, Nicholas Thompson <[hidden email]> wrote:
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Ted Carmichael
Indeed! The best example of that principle in action.
N
Nicholas S. Thompson
Emeritus Professor of Psychology and Ethology,
Clark University ([hidden email])
http://www.cusf.org [City University of Santa Fe]
============================================================ 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 |
Re: Real-world genetic algorithm example... help!
|
|
In reply to this post by Douglas Roberts-2
Or any other on-line forum since USENET and BBSes.
Ray Parks [hidden email] Consilient Heuristician Voice: 505-844-4024 ATA Department Mobile: 505-238-9359 http://www.sandia.gov/scada Fax: 505-844-9641 http://www.sandia.gov/idart Pager:505-951-6084 On 7/9/10 7:32 AM, Douglas Roberts wrote: > You do realize how much that sounds like a description of FRIAM, don't you? > > ;-} > > --Doug > > On Fri, Jul 9, 2010 at 7:17 AM, James Steiner <[hidden email] > <mailto:[hidden email]>> wrote: > > I remember it too! It seems that individual high producers were also > bullies, tending to stomp on other hens' eggs and attack the other > hens. > > ============================================================ 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 |
«
Return to Friam
|
1 view|%1 views
| Free forum by Nabble | Edit this page |