Mission: Increase Density

While my first experiments with Genetic Algorithms (GA) and floor plan optimization didn't exactly pass with flying colors, I decided to try with simpler problems first.

The most simple problem I could think of, was: each subject is represented with a randomly distributed set of points in space. Fitness funcion: the subjects with more points nearer to the center of the space are more fit.

After running GA several thousand times, the "density" of points was increased as expected. But even then, the local optimum problem stopped the process short of compressing the cloud of points into single point at the center of space (which would give the subject best fitness assessment).

I experimented a lot with different stategies, trying to overcome obstacles in the GA. As the theory suggested, proper mutations are the key weapon in fighting the local optimum problem. I added several "complications" in my mutation system: parametrization and randomization of when, where and how the mutations occur.

The results were improved, however, I must admit that my GA can't achieve the destined goals yet.