  Generative Adversarial Networks (GANs) are proving to be a powerful indirect
genotype-to-phenotype mapping for evolutionary search, but they have
limitations. In particular, GAN output does not scale to arbitrary dimensions,
and there is no obvious way of combining multiple GAN outputs into a cohesive
whole, which would be useful in many areas, such as the generation of video
game levels. Game levels often consist of several segments, sometimes repeated
directly or with variation, organized into an engaging pattern. Such patterns
can be produced with Compositional Pattern Producing Networks (CPPNs).
Specifically, a CPPN can define latent vector GAN inputs as a function of
geometry, which provides a way to organize level segments output by a GAN into
a complete level. This new CPPN2GAN approach is validated in both Super Mario
Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites
demonstrates that CPPN2GAN can better cover the space of possible levels. The
layouts of the resulting levels are also more cohesive and aesthetically
consistent.
