Neuroevolutionary Constrained Optimization for Content Creation

This paper presents a constraint-based procedural
content generation (PCG) framework used for the creation of
novel and high-performing content. Specifically, we examine
the efficiency of the framework for the creation of spaceship
design (hull shape and spaceship attributes such as weapon and
thruster types and topologies) independently of game physics
and steering strategies. According to the proposed framework,
the designer picks a set of requirements for the spaceship
that a constrained optimizer attempts to satisfy. The constraint
satisfaction approach followed is based on neuroevolution;
Compositional Pattern-Producing Networks (CPPNs) which
represent the spaceship’s design are trained via a constraint-based
evolutionary algorithm. Results obtained in a number
of evolutionary runs using a set of constraints and objectives
show that the generated spaceships perform well in movement,
combat and survival tasks and are also visually appealing.