  Automatic generation of level maps is a popular form of automatic content
generation. In this study, a recently developed technique employing the {\em do
what's possible} representation is used to create open-ended level maps.
Generation of the map can continue indefinitely, yielding a highly scalable
representation. A parameter study is performed to find good parameters for the
evolutionary algorithm used to locate high-quality map generators. Variations
on the technique are presented, demonstrating its versatility, and an
algorithmic variant is given that both improves performance and changes the
character of maps located. The ability of the map to adapt to different regions
where the map is permitted to occupy space are also tested.
