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 <http://www.bostondynamics.com/robot_bigdog.html>... 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 <[email protected]> wrote: > 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]> 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]> 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]>*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]<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_> >>> > 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]<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_> >>>> >* 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]<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_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 < >>>>> [email protected]<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_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 >>>>>> ([email protected]<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_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<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_129b987e5d851537_> >>>>>> *To: *The Friday Morning Applied Complexity Coffee >>>>>> Group<#129bb1f039123408_129bafc5a3e443b4_129ba189d8601434_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 >>> >>> 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
