Extended Data Fig. 3: Our implementation of the Goal Program Generator model fills the archive quickly and finds examples with human-like fitness scores.

Left: Our model rapidly finds exemplars for all archive cells (that is niches induced by our behavioral characteristics), reaching 50% occupancy after 400 generations (out of a total of 8192) and 95% occupancy after 794 generations—the archive is almost full 1/10th of the way through the search process. Middle: Our model reaches human-like fitness scores. After only three generations, the fittest sample in the archive has a higher fitness score than at least one participant-created game. By the end of the search, the mean fitness in the archive is close to the mean fitness of human games. Right: Our model generates the vast majority of its samples within the range of fitness scores occupied by participant-created games, though few samples approach the top of the range.