  When generating content for video games using procedural content generation
(PCG), the goal is to create functional assets of high quality. Prior work has
commonly leveraged the feasible-infeasible two-population (FI-2Pop) constrained
optimisation algorithm for PCG, sometimes in combination with the
multi-dimensional archive of phenotypic-elites (MAP-Elites) algorithm for
finding a set of diverse solutions. However, the fitness function for the
infeasible population only takes into account the number of constraints
violated. In this paper we present a variant of FI-2Pop in which a surrogate
model is trained to predict the fitness of feasible children from infeasible
parents, weighted by the probability of producing feasible children. This
drives selection towards higher-fitness, feasible solutions. We demonstrate our
method on the task of generating spaceships for Space Engineers, showing
improvements over both standard FI-2Pop, and the more recent multi-emitter
constrained MAP-Elites algorithm.
