Interesting application of genetic algorithms. The weird thing about GA though, and any optimiser, is that you need an "error function" that you will us to determine when one solution is better than another. Presumably he designed an error function for "fun", and his GA system finds local minima within it. But this is what I mean by "weird thing": you have to come up with a quantification of what makes a game fun, and _this_ is ultimately what will determine what kinds of solutions you come up with. Regardless of how the optimiser works, the error function is what the programmer is designing, and therefore the programmer is designing the space in which solutions will exist, so I would argue that any specific games are attributed to the programmer (or whoever designed the error function), and any specific game just happens to be one choice within the available search space.
Unless you want to manually try every iteration and rate it from 1 to 10, this implies that it's possible to come up with a "funness" model of human game playing. That in itself is sort of a interesting thing, falling somewhere between philosophy and psychology, rather than computer science.
Unless you want to manually try every iteration and rate it from 1 to 10, this implies that it's possible to come up with a "funness" model of human game playing. That in itself is sort of a interesting thing, falling somewhere between philosophy and psychology, rather than computer science.