It is not quite a genetic algorithm: there is no population of individuals s which get mated and possibly mutated. It's more of a dynamic programming polygon match. Still, the result is impressive and amusing.
Exactly, the 'genetic' part of the term implies some sort of breeding, not that you can transform your search space into a vector which you call a genome. In fact, the algorithm used seems to be exactly what is described on the wikipedia for 'Random Optimization' [1].
I would expect that a true GA might work better, but not be the best choice. In my semi-related experience, Particle Swarm Optimization [2] works much better for continuous valued problems.
