Back in the 00s a friend and I played with the idea of using genetic programming and random line segments to approximate images (ie. maintain a population of candidates, take the best n percent, "breed" them to create the next generation, add some "mutations", repeat). This implementation seems to use a basic hill-climbing algorithm to reach a local optimum – something like simulated annealing might improve both quality and speed of convergence.