Surprising Creativity: Anecdotes from Evolutionary Computation

Post by jcims » Fri, Mar 16, 2018, 6:28 PM UTC

Post by [deleted] » Fri, Mar 16, 2018, 7:06 PM UTC

fallous wrote: post #7

sago wrote: post #3

Earlier quoted context omitted.

That is one kind of problem. The 'Genie' problem: be very specific what you wish for because the genie will misinterpret you. But it's not the only kind of problem. There are inherent biases in evolutionary systems. One example: in systems with varying length genotypes there is a massive pressure to bloat. Even if you encode 'small' as a strong requirement in your fitness function, it may not be enough (or it may be…

Just picking a nit, but the problem with a genie isn't that it misinterprets you but that it literally does exactly what you ask for. The mismatch between "what you wanted" and "what you asked for" isn't the genie's fault but yours.

Post by dahart » Fri, Mar 16, 2018, 8:33 PM UTC

abecedarius wrote: post #15

dahart wrote: post #4

The article's philosophical discussion of "surprise" feels a little klunky and academic to me, and I think conflates some different kinds of surprise, but it's still really fun to think about. Many of the projects here are all inspired by Karl Sims' work in the early 90's. http://www.karlsims.com/ Sims evolved virtual creates by specifying goals to achieve and then running a physics simulation. He noted at the time t…

Nice work! Your inspired-by-Sims collection is the best I've seen in that genre, I think. I always look when someone links to their genetic images because I once made some too: https://djb.deviantart.com/gallery/ (They were less garish at the original gamma setting, that's how long ago it was.) It always felt like there's more potential in this direction. Maybe with a magic sprinkling of neural nets?

Post by abecedarius » Fri, Mar 16, 2018, 9:02 PM UTC

dahart wrote: post #16

abecedarius wrote: post #15

Earlier quoted context omitted.

Nice work! Your inspired-by-Sims collection is the best I've seen in that genre, I think. I always look when someone links to their genetic images because I once made some too: https://djb.deviantart.com/gallery/ (They were less garish at the original gamma setting, that's how long ago it was.) It always felt like there's more potential in this direction. Maybe with a magic sprinkling of neural nets?

Hey thank you!! Yours look great too. I know what you mean about gamma, mine were probably a little more washed out 15 years ago, I accidentally made them a bit gamma-proof I think. ;) I’m dying to find the time to do a JavaScript+WebGL version of this that other people can play with. The big thing I spent time on was animating cross-fades between evolved expressions. I’ll send a link to the vid & paper if you’re int…

Post by tabtab » Fri, Mar 16, 2018, 9:33 PM UTC

sago wrote: post #3

mring33621 wrote: post #2

When using evolutionary pressures to guide software, you have to remember that your stated goal (via fitness functions) often does not truly reflect your intended goal. For example, when I was using genetic algorithms to pick stock trades, I tried to maximize total_net_gain_loss / number_of_trades. The GA quickly figured out that 0 trades was the best answer. In hindsight, Duh! But I wanted to make trades! "The only…

That is one kind of problem. The 'Genie' problem: be very specific what you wish for because the genie will misinterpret you. But it's not the only kind of problem. There are inherent biases in evolutionary systems. One example: in systems with varying length genotypes there is a massive pressure to bloat. Even if you encode 'small' as a strong requirement in your fitness function, it may not be enough (or it may be…