None of the “Genetic” or “Simulated annealing” algorithms have anything to do with what they claim. Neither of them are any more than naive hill climbing ( http://en.wikipedia.org/wiki/Hill_climbing ).

Seriously, this is about the worst method of doing this. The least you could do is add some momentum to the simulation to speed it up and avoid local maxima.

Posted at 10:34 pm on Dec 10th, 2008 by RyanT

This bs again?
Pleh.

Posted at 11:34 pm on Dec 10th, 2008 by marz

I have to admit, these things are at least addicting to watch.

I took this chance to upgrade to the latest build of firefox. This performance in this script was noticeably faster. Cool.

Posted at 11:40 pm on Dec 10th, 2008 by Eric

i’d like to see some sequenced porn, might hold the crowd a little…

Posted at 12:04 am on Dec 11th, 2008 by Soundwavehi

http://i38.tinypic.com/2m3h6zc.png
hackaday logo after 3 hours.

Posted at 12:19 am on Dec 11th, 2008 by JackHack

This sounds like a bog standard ‘greedy’ hill climbing algorithm to me, which has nothing to do with simulated annealing.

Simulated annealing would involve a probability of choosing an image which is -less- like the target image. This prevents the algorithm getting stuck in dead-ends (local maxima).

Posted at 12:48 am on Dec 11th, 2008 by icept

I made my own evolution script. It was tested in OSX but should work in Linux too. It may work in windows but the terminal wont clear it’s self. It is a LOT more simple then the annealing one. It only uses a string and not a picture.

http://dl-client.getdropbox.com/u/30726/evo.py

Posted at 1:06 am on Dec 11th, 2008 by t0ny@t0ny.org

It crashed my Firefox after a minute.

Posted at 2:49 am on Dec 11th, 2008 by DarkFader

This could be some great AI for a painting-bot

Posted at 6:12 am on Dec 11th, 2008 by Jess

I think this is cool. Efficient LED blinkers get old after a while..just kidding.

I seen a project years back using genetic programming that generated vectorized c++, compiled it, analyzed it’s efficiency, and generated a new algorithm until it couldn’t get anymore efficient.

Posted at 6:27 am on Dec 11th, 2008 by TJHooker

How is this best fit? Clearly, looking at the image there is less than 50 polygons. I’m not even sure how this is genetic programming. What DNA do you start out with? If it is random then how do you decide what is possible? How does the program know which is the best match? If you are doing this by eye then really your program is not truly genetic. If you keep randomly changing points of the polygon eventually you will end up with something similar. How do you decide the coloring or is that random too? Why doesn’t the program learn and evolve leaving out any interaction other than the input of an image? Then spit out the final best fit. The term genetic programming is used quite loosely here. What is the environment that the program is in? How does it evolve? This is basically randomizing polygons until you get a best fit image by eye.

Posted at 6:50 am on Dec 11th, 2008 by max

Max, RTFA. It starts with 50 randomly placed polygons, all of them invisible. That’s why you don’t see them all. Best match is determined by a pixel-by-pixel comparison of the test image with the original image. The more color difference there is, the larger the difference. Differences are summed and a smaller total is better. A mutation is a random change to either one pixel or the color of a single polygon. As to whether it’s GP or not, you’re entitled to your own opinion. The author says: “I will claim that this is a GP due to the fact that the application clones and mutates an executable Abstract Syntax Tree (AST). Even if the population is small, there is still competition between the parent and the child, the best fit of the two will survive.”

Posted at 6:58 am on Dec 11th, 2008 by Dan

icept is 100% correct: this is Hill Climbing, not Simulated Annealing. It’s also completely unrelated to Genetic Algorithms.

I expect crappy science reporting from other places, not Hack a Day. Eliot, you should really know better.

Posted at 7:38 am on Dec 11th, 2008 by Phoenix

phoenix, based on the link you provided, hill climbing “…starts with a random (potentially poor) solution, and iteratively makes small changes to the solution, each time improving it a little…” This implementation does not do that. It makes a random change, checks to see if it’s more fit, and then either keeps or discards the change. That’s a mutation and a fitness function and that puts it in the realm of GP.

Posted at 7:43 am on Dec 11th, 2008 by Dan

In my response to max, that should read “A mutation is a random change to either one _vertex_ or the color of a single polygon…”

Posted at 7:50 am on Dec 11th, 2008 by Dan

Wait, what?
WHY IS THIS ON HACKADAY?!
If you want to learn about genetic algorithms just read Wikipedia you mofos!

Posted at 8:29 am on Dec 11th, 2008 by Ron Paul

Dan, so what you’re saying is the image is ideal DNA for the environment. The program searches or mutates until a DNA is best fitted based on parameters like polygons and iterations. Fair enough, I still don’t think it’s GP. Oh well, semi-interesting anyways.

Posted at 9:02 am on Dec 11th, 2008 by Max

Man, how the hell can anyone claim that blurring
up a picture to the point that you can’t see any
details, qualifies as “BEST” ???? WTF !??? did
someone reverse the captions ? If I want to screw
up my focus algorithims for a new digital camera,
I’ll just smear up the lens with grease! we don’t
need no stinkin “annealing” BS to hose up a picture!

Posted at 10:01 am on Dec 11th, 2008 by JohnQ

is this a “hack” no, Is it good for anything usefull? Not really. Is it worthy of hack a day? No.

Posted at 10:54 am on Dec 11th, 2008 by Clay

This stuff is ubercool — especially when coded in client-side javascript.

Is it good for anything useful? That’s a bit like asking if science is good for anything useful. If you’re not sure about the answer to that question then I don’t know what to tell you. The example presented here is geared toward a pretty specific problem but, then again, so is nearly every post that has to do with a specific piece of equipment/device/programming framework/whatever. Stretch your imagination a bit (or just use google) and I’m sure you’ll find plenty of applications for this sort of thing.

Posted at 1:22 pm on Dec 11th, 2008 by Andrew

I wrote my own GA version and actually, my results were very similar. The problem is that there is a lot of interdependences between the genes/triangles especially when you have transparency involved. So you need to have, multiple changes in color and point location to have an improvement that is propagated. Which doing a single mutation at a time won’t allow.

In summary its easy to get into local minimum and not reach the global ones.

I think the first one did such a good job because his mutations did a much better job of getting of local minimum.

Posted at 1:29 pm on Dec 11th, 2008 by jhaluska

http://www.blprnt.com/smartrockets/

Posted at 8:51 pm on Dec 11th, 2008 by ReKlipz

I have to agree: this is just a hill climbing algorithm, aka “I failed algorithms 101″. This really has no relation to genetic algorithms.

Posted at 12:20 am on Dec 12th, 2008 by ccox

Darmowe ogloszenia- z nami sprzedasz, kupisz kazda rzecz.Nie musisz zakladac konta, w prosty sposob zamiescisz bezplatnie swoje ogloszenie.
Dolozylismy wszelkich staran by serwis dzialal bez blednie.
Zapraszamy do odwiedzenia naszego serwisu http://www.ogloszenia-lubelskie.pl

Posted at 11:31 am on Dec 17th, 2008 by ogloszeniaaa