> Hill Climbing or Simulated Annealing for optimization (hill climbing multiple random shapes is nearly as good as annealing and faster) Great to see the autho

> Hill Climbing or Simulated Annealing for optimization (hill climbing multiple random shapes is nearly as good as annealing and faster) Great to see the author is using this instead of the GA / evolutionary algorithm code it was inspired by. You can see it in the results too, they are gorgeous! Annealing (and, apparently hill-climbing too) are just faster to converge, allowing to tweak better optimizations for the hyperparameters. GAs are an attractive idea because, well, if you look around, evolution gives rise to such clever solutions and beautiful shapes. But people tend to forget natural evolution took billions of years, especially (sort of) the big leaps in complexity. Annealing is almost always a better solution, with the possible exception if your problem space is biologically inspired and you come up with a sensible crossover operation. If the crossover operation (or without one) doesn't have the right properties, a GA is basically equivalent to annealing (except in parallel), but much harder to tweak for fast convergence.