Genetic Algorithm Used to Build Car With Box2D

http://megaswf.com/serve/102223/

by solipsist • 16 years ago

81 28 16 years ago

megaswf.com

sorbus• 16 years ago

Link to the Reddit discussion: http://news.ycombinator.com/item?id=2127367

keyle• 16 years ago

That's so funny I spilled my coffee.

solipsist5• 16 years ago

I've let it run for 10 generations in the background, and keep coming back to be entertained. It's fun to see the cars tackle the course. I've already gotten attached to a few of them - I cheer them on when they make it over a hill and cry with despair when they don't. =)

Here is one car that I got in generation 10: http://i.imgur.com/J0kdV.png

It didn't do too well, but it reminded me of a [update] penny-farthing. This should be familiar to people living in Palo Alto: http://www.paloaltoonline.com/news/show_story.php?id=11781%3...

joshu• 16 years ago

(the bike is a penny-farthing. http://en.wikipedia.org/wiki/Penny-farthing )

solipsist5• 16 years ago

Thanks for pointing that out. To be technical, though, it is a reverse penny-farthing.

templaedhel• 16 years ago

I let it run overnight, getting up to 60 generations. I cannot tell if the cars are actually improving that much, but the graph says they are. I saw one get to 245, but I missed that screenshot. Unfortunately a lot of the good cars spawn with their wheels upside down, or rotated, I wonder if spawn orientation is evolving along with the car. It would appear not. Here is the best car of the 62 gen.

http://i.imgur.com/H1Rrw.png

solipsist5• 16 years ago

I did that too and got it up to the 114th generation. Unfortunately, the car has gotten stuck and the game seems to be going nowhere. So I'll never see the 115th generation...

http://i.imgur.com/bAnmY.png

reitzensteinm5• 16 years ago

I recently built a game with similar mechanics (http://www.bigblockgames.com/games/goblin/), and this is basically what my first day of development looked like, just sped up between takes.

Except at least this doesn't create many cars unstable enough that the structure starts oscillating before shooting off to infinity. So I guess GA 1, Michael 0.

steverb• 16 years ago

Found that game earlier this week due to BoingBoing. Nice job.

reitzensteinm5• 16 years ago

Thanks :)

Vivtek3• 16 years ago

I don't normally get into video games, but dear Lord I like that one.

reitzensteinm5• 16 years ago

Cheers dude :)

Our philosophy is basically to make flash games that don't suck! Goblin seems to have gotten quite a bit of attention over the past 72 hours or so.

RevRal• 16 years ago

Might enjoy this: http://www.youtube.com/watch?v=mcAq9bmCeR0 .

Vivtek3• 16 years ago

I've seen that before - there doesn't seem to be anywhere that you can see his code, especially his selection function. I'd find that pretty interesting.

mambodog• 16 years ago

I previously submitted this similar (old) demo: http://www.qubit.devisland.net/ga/

huherto• 16 years ago

This was very nice. I remember this, we spend a lot of time just watching it.

aothman• 16 years ago

Cool demo, but from a practical perspective there's absolutely no reason to use GAs to solve a non-linear optimization problem. I know people like them because they have a really pleasing and intuitive backstory, but as a grad student in AI I can tell you that they suck at actually solving anything. Mostly this is due to the fact that GAs take what is already a difficult, non-linear problem (the problem you're trying to solve) and immediately, explosively, complicate it (what's your mutation rate? what are the chromosomes? how are crossovers handled? how are you deciding the answers to these questions?)

Vivtek3• 16 years ago

Didn't I read relatively recently about a patent filed on something discovered using a GA? Maybe for lens design?

stipes• 16 years ago

I believe Koza has several patents for things he has discovered using Genetic Programming / Algorithm techniques.

Andys• 16 years ago

Solution: Use a GA to evolve the GA parameters. I'd better go and patent this brilliant idea ...

mzl• 16 years ago

Well, to be fair the parameter problem is common for all kinds of local search and is an interesting research topic. That being said, I agree that GAs are seldom the most efficient, other kinds of local search are typically better.

For problems where there is a natural way to express chromosomes, mutations, and crossover that captures interesting and relevant information GAs can be useful as an additional meta-heuristic.

bermanoid• 16 years ago

I think the real problem with most uses of GAs (including this one) is that to the extent that they work at all, they're merely performing a random walk search driven by mutation, and the "crossover" operation is, for all intents and purposes, just functioning as a random-restart of the search, a jump to a new spot in parameter space rather than a useful shuffling of units of functionality.

Sometimes a random local search is exactly what the doctor ordered, of course, it's almost entirely trivial to code and especially if you tune the variance over time you can get pretty good results, though I'd agree that in general you can usually get better results in less computer time with a more typical algorithm (though you've still got to choose the algorithm, implement/integrate it, test it, etc., so you lose a lot of programming time unless it's already part of your framework).

Genetic algorithms should really shine when it's not clear how to cast the problem as a finite dimensional optimization problem, for instance if you're trying to evolve some constructive procedure to solve a problem rather than tweak variables to improve performance. You're absolutely right, aothman, most problems, even most problems specifically constructed to show off GAs, don't fit into this category, so there's no real point except that the algorithm sounds cool...

solipsist5• 16 years ago

1st generation

  #0  : 6
  #1  : 1.7
  #2  : 0.5
  #3  : 0.4
  #4  : 0.9
  #5  : 0
  #6  : 2.6
  #7  : 0
  #8  : 0
  #9  : 0
  #10 : 0.4
  #11 : 6.1
  #12 : 0.2
  #13 : 0.7
  #14 : 1.7
  #15 : 0
  #16 : 0
  #17 : 1.4
  #18 : 0
  #19 : 1.3

  Average : 1.2

  One of the cars from generation 43: http://i.imgur.com/95QvW.png

43rd generation (after hours of waiting!)

  #0  : 146.3
  #1  : 211.6
  #2  : 237
  #3  : 235.8
  #4  : 0
  #5  : 128.4
  #6  : 0
  #7  : 0
  #8  : 204.4
  #9  : 137.7
  #10 : 85.9
  #11 : 208
  #12 : 211.1
  #13 : 36.3
  #14 : 0
  #15 : 202.8
  #16 : 247
  #17 : 239
  #18 : 230.2
  #19 : 139.5

  Average : 145.1

  One of the cars from generation 43: http://i.imgur.com/WZtZq.png

Now that's what I call evolution!

Genetic Algorithm Used to Build Car With Box2D

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