A Self-Adaptive Evolutionary Approach to the Evolution of Aesthetic Maps for a RTS Game

Abstract

Procedural content generation (PCG) is a research eld on the rise,with numerous papers devoted to this topic. This paper presents a PCG method based on a self-adaptive evolution strategy for the automatic generation of maps for the real-time strategy (RTS) game PlanetWars. These maps are generated in order to ful ll the aesthetic preferences of the user, as implied by her assessment of a collection of maps used as training set. A topological approach is used for the characterization of the maps and their subsequent evaluation: the sphere-of-in uence graph (SIG) of each map is built, several graph-theoretic measures are computed on it, and a feature selection method is utilized to determine adequate subsets of measures to capture the class of the map. A multiobjective evolutionary algorithm is subsequently employed to evolve maps, using these feature sets in order to measure distance to good (aesthetic) and bad (non-aesthetic) maps in the training set. The so-obtained results are visually analyzed and compared to the target maps using a Kohonen network.