

Collaborative Evolution: The Mona Lisa in Canvas - peterbraden
http://peterbraden.couchone.com/lisa/_design/lisa/index.html

======
lmkg
I really like this because I can see the evolution taking place, including all
the strange mutations. Reading synopses of genetic algorithms is often
somewhat boring: "after 2000 generations, I had a fitness ranking of 54, but
after another 3000 generations it had settled around 56!" The frustration
boils down to the fact that they lack a story of how the algorithm arrived at
its answer. It just has an answer, and it happens to work, and it's
inscrutable. It's cool, because it works, but its opacity makes it a poor
spectator sport.

Here, I get to see an entire story. I see innovation, progress, bold new ideas
be attempted and fail. Of course, the story is all in my head, but I feel like
I can see how it works, and what it's trying to do. My browser can't handle
enough generations, but maybe after watching it more I could see what it's
good at and what it struggles. Never underestimate the power of
visualizations.

(aside: link got borked?)

------
moultano
Surely there's a more interesting way to do this than random variations and
picking the best. Why not constrain the color of the circle to the one that
maximizes your objective function given its position? That isn't hard to
compute.

~~~
hyyypr
"Surely there's a more interesting way to do this than random variations and
picking the best".

The whole point behind genetic algorithms is precisely, randomness (mutations)
and picking the best (fitness). Constraining the mutations would maximize the
chances of finding a local optimum.

------
peterbraden
oops, couchone has gone down. The source is on github at
<https://github.com/peterbraden/genetic-lisa>

~~~
peterbraden
and back up. I have no idea how much load I'm sending them, but apologies in
advance!

