Evolving Diverse Cellular Automata Based Level Maps

  title={Evolving Diverse Cellular Automata Based Level Maps},
  author={Daniel A. Ashlock and Matthew Kreitzer},
This chapter generalizes a technique for creating terrain maps using a generative fashion based cellular automata representation. The original technique, using fashion based cellular automata, generated terrain maps that exhibit a consistent texture throughout. The generalization presented here co-evolves rules to permit a spatially varying type of map. Pairs of fashion based cellular automata rules are evaluated with objective functions that require connectivity within the terrain and… 

Automatic Generation of Diverse Cavern Maps with Morphing Cellular Automata

This study co-evolves pairs of competition matrices to permit the evolution of automata rules that can be spatially morphed to provide substantially more diverse types of maps than earlier systems using fashion-based cellular automata.

The Riddle of Togelby

This study examines the possibility of enriching search spaces so that they contain very high rates of interesting objects, specifically game elements, and highlights a number of potential avenues.



Evolvable fashion-based cellular automata for generating cavern systems

  • D. Ashlock
  • Computer Science
    2015 IEEE Conference on Computational Intelligence and Games (CIG)
  • 2015
This study introduces fashion-based cellular automata as a new representation for generating cavern-like level maps by performing a parameter study and demonstrating a robustness of the fashion based representation to the variation of parameters.

Rescalable, Replayable Maps Generated with Evolved Cellular Automata

This study extends an earlier study, examining new fitness functions and studying reusability, scalability, and the impact of parameter tuning for this type of cellular automata for automatically designing level maps.

Fitness Landscapes of Evolved Apoptotic Cellular Automata

This paper examines the fitness landscape for evolutionary algorithms evolving cellular automata (CA) rules to satisfy an apoptotic fitness function and introduces the evolution of apoptotic CA as a test problem and evolved art technique.

Search-Based Procedural Generation of Maze-Like Levels

This study compares multiple representations for evolvable mazes including direct, as well as positive and negative indirect representations, and gives a simple framework for designing fitness functions that permits substantial control over the character of the mazes that evolve.

Research of Complexity in Cellular Automata through Evolutionary Algorithms

The results show that the genetic algorithmic is a promising tool for the search of cellular automata with specific behaviours, and then could prove to be decisive for identification of new automata supporting universal computation.

Cellular automata for real-time generation of infinite cave levels

A simple CA-based algorithm is evaluated on an infinite cave game, generating playable and well-designed tunnel-based maps.

Simultaneous Dual Level Creation for Games

The system is shown to be able to design dual mazes whose properties depend substantially on both the choice of fitness function and representation used, and two representations for game levels with multiple barrier types using four different pairs of fitness functions.

Towards multiobjective procedural map generation

A search-based procedural content generation (SBPCG) algorithm for strategy game maps is proposed, along with a number of objectives relating to predicted player experience, which generates Pareto fronts showing how these objectives can be balanced.

Search-Based Procedural Content Generation

A taxonomy of stochastic search algorithms used to generate game content, centring on what sort of content is generated, how the content is represented, and how the quality of thecontent is evaluated is proposed.