  This paper introduces MicroRCT, a novel open source simulator inspired by the
theme park sandbox game RollerCoaster Tycoon. The goal in MicroRCT is to place
rides and shops in an amusement park to maximize profit earned from park
guests. Thus, the challenges for game AI include both selecting high-earning
attractions and placing them in locations that are convenient to guests. In
this paper, the MAP-Elites algorithm is used to generate a diversity of park
layouts, exploring two theoretical questions about evolutionary algorithms and
game design: 1) Is there a benefit to starting from a minimal starting point
for evolution and complexifying incrementally? and 2) What are the effects of
resource limitations on creativity and optimization? Results indicate that
building from scratch with no costs results in the widest diversity of
high-performing designs.
