imclab / genetic-lisa

The Evolution of a Smile

A collaborative evolutionary algorithm that aims to reproduce the Mona Lisa with overlapping translucent circles.

Introduction

In December 2008, Roger Alsing wrote a program that used a genetic algorithm to evolve an approximation of the mona lisa using 50 overlapping polygons. The results were very impressive and his report generated a lot of interest.

I was curious to try it myself, and wrote a quick version in python. At the time, the source for Roger's program wasn't available (he's subsequently released a .NET program with source, although I haven't looked at it), so I guessed at how the program worked.

Instead of using a fixed set of polygons, I decided to use ellipses, initially because the maths was easier, but subsequently because I like the abstract 'lava lamp' look of the intermediary images. Rather than use a fixed array of shapes like Roger, I used a fitness function that encouraged less shapes, and allowed my mutation operation to add or remove ellipses.

Despite experimenting with a bunch of different image libraries, and using psyco, the python program was prohibitively slow, only evolving a few thousand generations per hour. I experimented with generation populations, varying between 10 and 100 - it was interesting looking at how adjusting the parameters affected the speed at which the fitness improved.

As the bottleneck in the program seemed to be the graphics library, I decided to rewrite the program in c, using arrays of pixels to manipulate so the fitness function would be blazing fast. Despite my rusty C skills, the many memory leaks, and platform inconsistencies (OS X zero fills malloc allocations, linux does not), I got a version running and left it overnight on a friends server.

Using a generation population of 100, in 19000 generations I had something that looked vaguely Da Vinciesque. If you squinted, it looked pretty good. Curiosity satisfied, I abandoned the project.

A year later and I'm playing with html5. I'm interested in splitting computation between multiple browsers. One of my projects, a collaborative map-reduce raytracer, uses multiple browsers to render an image into canvas. I'm excited by the capabilities of canvas, and start writing more and more javascript. My server side code, a mess of python and mysql, limits the interest I have in doing anything further with this.

A few months ago I started toying with the idea of using a couchapp to simplify the collaborative processing backend. Looking through my old projects, the mona lisa code seems perfect for this, so I rewrite the genetic algorithm in javascript with the canvas.

This time I'm using circles instead of ellipses. And I add the ability to cross breed between browsers. In 10,000 generations or so, I'm getting interesting images. And this time, you can see the images evolving.

Check it out

Running on couchone here.