Bots and Simulations > Evolution and Internet Sharing Sims
A little evolution for everyone!
Numsgil:
--- Quote ---Bots adapt to the environment, which is made of, say, the physics settings AND the other bots; however, you can suppose that, except from particular cases, most of the adaptation is towards the physical environment.
--- End quote ---
That's an assumption that hasn't been really tested. And in a good scientific experiment, you want to isolate all other possible variables except the one you're testing.
You may be right, you may be wrong. No one has really devised an experiment to decide one way or another. So you have to assume that bots do adapt against each other and plan your experiment accordingly.
For an informal study, you're welcome to make such an assumption, but my critiques are all on the methodology. I'm trying to think and comment the same way scientists do to each others' papers.
--- Quote ---No, that's wrong. Humans have a great variability, so you can take very successful humans and suppose that they have something tha makes them so successful. But robots are much simpler, their behaviour is totally determined by the dna (and they have no sexual reproduction, so no dna remixing) so thata successful robot is successful either by pure chance or because it is, alone, a new species, in the sense that it has some important mutation.
--- End quote ---
True, true, the analogy isn't perfect. But what makes a human successful today and what made a human successful 10000 years ago are very different (or somewhat different, some things never change). Humans haven't really had a chance to adapt in that time, but imagine they have. A successful human 10000 years ago was a good hunter, or perhaps a good farmer (was agriculture arouynd 10K years ago?). A successful human today is probably very good at finances, perhaps at diplomacy and "playing the game".
Again, a good study isolates all but the variable you're testing.
--- Quote ---Why instead, as I suggested, don't you try to run a looong simulation with mutrate of mutrates at 1 or so, so to evolve the best mutation rates?
--- End quote ---
That would be an interesting experiment too. But you'd probably need to run the sim longer. Maybe like 10 000 000 cycles?
shvarz:
It is a good idea to run these kinds of experiments, although interpretation may be a problem as we can see already :)
As Nums mentioned, you need to express mutation rates as N/command. You also need to keep track of your average population size - the bigger the population, the higher mutation rate it can tolerate (up to some limit).
And interpretation still may be a problem... I actually don't agree with Nums idea that putting two bots in competition does not represent their relative fitness (because they were not adapted to each other). While it may be true, it should not have a large effect if bots are still relatively similar. But I do agree that you need to run the same experiement at least three times and come to conclusion only if you see the same trend in all three simulations. There is quite a lot of variability in DBs - the population is small, the environment is small, the DNA is short - this all leads to a lot of noise.
Finally:
--- Quote ---Why instead, as I suggested, don't you try to run a looong simulation with mutrate of mutrates at 1 or so, so to evolve the best mutation rates?
--- End quote ---
This is an interesting experiment, but I have no idea what it is going to do. My head hirts when I try to think about all the stuff that is going to be involved in that. Thing with mutrate of mutrates is that mutrate has no advantage for current generation - it only affects the next generation and even then not directly. In general, organisms don't want to mutate - chances of getting better after mutations are minute. So the selection should drive to eliminate mutation rates completely. I guess.. I may be wrong...
Carlo:
--- Quote ---
--- Quote ---Why instead, as I suggested, don't you try to run a looong simulation with mutrate of mutrates at 1 or so, so to evolve the best mutation rates?
--- End quote ---
This is an interesting experiment, but I have no idea what it is going to do. My head hirts when I try to think about all the stuff that is going to be involved in that. Thing with mutrate of mutrates is that mutrate has no advantage for current generation - it only affects the next generation and even then not directly. In general, organisms don't want to mutate - chances of getting better after mutations are minute. So the selection should drive to eliminate mutation rates completely. I guess.. I may be wrong...
--- End quote ---
Well, basically the mutation rates determine the probability of having an offspring more or less mutated. This simply means that they affect the probabilty of the offrspring being more or less successful. For each set of mutation rates, say that you have these three values:
a- probability for the offspring to be identical to the parent
b- probability for the offspring to be worse
c- probability to be better
Say that a realistic mutation rates set may have a=90%, b=9.98%, c=0.02%. The interest of the robot is to have a successful offrspring, so it has to maximize c while keeping a good ratio between a and b, which can be done by regulating the mutation rates set. So you can treat it like a mutation as every other: evolution will try to optimize mutation rates to give the better a, b, c ratios.
About the idea that evolution tries to eliminate mutation: it's wrong! As long as an organism has the ability to mutate, it can find some good mutation to be better than its competitors. Let's reason in terms of lions and gazelles. If evolutions succeeds in eliminating mutation in lions, gazelles, which still mutate, can always develop the ability to run faster. No lion will ever catch them again, since lions lost the ability to mutate. A gene which gives gazelles the ability to mutate is successful, because allowing the mutation of the genes for speed in the gazelle gene pool, has more probability to be passed to the faster gazelles, which survive better.
You can do this little experiment, anyway (I tried, and seemd to work - now that there are tools for leagues, should be even easier): just put in the same environment two copies of the same species, identical except for the fact that one has reasonable mutation rates, while the other one has mutations disabled. When I tried it, the species unable to mutate was usually (on average) wiped out by the other one in more or less time.
But would be really great to make the experiment seriously: that is, with tens or hundreds of tries to have precise numbers (and maybe with a good esteem of the average time for a species to take over the other one, and how this time is affected by mutation rates... ). Something may be done automatically with the tools for leagues. And maybe Shvarz knows wheter somebody would be interested in a paper on the subject :boing:
Botsareus:
Shvartz I run a lot of simulations with m of m set to 5 this what happens:
we start off at m of m = 5 , the rest 300
we result with 5 of 5 = 13 , the rest minimum 225 maximum 410 , the interesting thing is that in most cases the same rates get the same numbers under any seed value you provide. And that the x in 1 / x of rates generaly increased
Numsgil:
I have run an F1 competition between identical copies of a species, except one had an extra eye reference comparison in an empty gene to make it look different. After many many many rounds, the modified version won.
Which shows that even identical copies will eventually produce a winner in F1 settings.
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