Evolvable fashion-based cellular automata for generating cavern systems

Abstract

Cellular automata can be used to rapidly generate complex images. This study introduces fashion-based cellular automata as a new representation for generating cavern-like level maps. Fashion-based automata are defined by a competition matrix that defines the benefit to a given cell state of having a neighbor of each possible cell state. A simple fitness function permits this type of automata to be evolved to produce a variety of level maps. A parameter study is performed and a variety of level maps are evolved with a toroidal grid, ensuring that the level maps tile. The parameter study demonstrates a robustness of the fashion based representation to the variation of parameters. The appearance of a given cavern-like level is encoded in the evolved automaton rule permitting the creation of many levels with a similar character simply by varying initial conditions. The cellular automata rules function in local neighborhoods meaning that the level generation system scales smoothly to any desired level map size.