Genetic Algorithms try to apply evolution mechanisms to find solutions to hard problems (typically, where no "proper" solution is known and where the search area is large).
Roger Alsing posted a couple of days ago an extremely cool article showing the convergence of 50 polygons to represent the Mona Lisa, using a random approach.
That was too cool to not try to implement it :)
The screenshot shows a rendering of the Mona Lisa using 50 polygons (16 points each), after 40818 total iterations, with 4577 elected states; the middle image is the original (i.e. the target) and the right image the difference between the current polygon-based image and the target (i.e. a representation of the fitness function).
Underneath was an earlier attempt using ovals instead of polygons.
Now, to be more exact, Roger Alsing's algo is more a hill climber algorithm or possibly a simulated annealing algorithm than a good example of a genetic algorithm; it should be interesting to actually implement a proper genetic algorithm approach (i.e. a population > 1) and see how the convergence rate compares... combining polygons and ovals might also result into interesting things.