Evolving Cellular Automata for Maze Generation

  • Andrew Pech
  • Philip Hingston
  • Martin Masek
  • Chiou Peng Lam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8955)


This paper introduces a new approach to the procedural generation of maze-like game level layouts by evolving CA. The approach uses a GA to evolve CA rules which, when applied to a maze configuration, produce level layouts with desired maze-like properties. The advantages of this technique is that once a CA rule set has been evolved, it can quickly generate varying instances of maze-like level layouts with similar properties in real time.


procedural content evolutionary algorithm cellular automaton 


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  1. 1.
    Toy, M., Wichmann, G.: Rogue. In: Cross-platform (ed.) Epyx (1997) Google Scholar
  2. 2.
    Team, N.D.: NetHack. In: Cross-platform (ed.) NetHack Dev Team (1987)Google Scholar
  3. 3.
    Koeneke, R.T., Moria, J.: In: Cross-platform (ed.). Abandonware (1994)Google Scholar
  4. 4.
    North, B.: Diablo. In: Cross-platform (ed.). Blizzard Entertainment (1996)Google Scholar
  5. 5.
    Ashlock, D., Lee, C., McGuinness, C.: Search-Based Procedural Generation of Maze-Like Levels. IEEE Transactions on Computational Intelligence and AI in Games 3, 260–273 (2011)CrossRefGoogle Scholar
  6. 6.
    Johnson, L., Yannakakis, G.N., Togelius, J.: Cellular automata for real-time generation of infinite cave levels. In: Proceedings of the 2010 Workshop on Procedural Content Generation in Games, pp. 1–4. ACM, Monterey (2010)CrossRefGoogle Scholar
  7. 7.
    Ashlock, D.: Cellular Automata Flavours, personal communication. (2013) Google Scholar
  8. 8.
    Joris, D.: Adventures in level design: generating missions and spaces for action adventure games. In: Proceedings of the 2010 Workshop on Procedural Content Generation in Games. ACM, Monterey (2010)Google Scholar
  9. 9.
    van der Linden, R.L., Bidarra, R.: Designing procedurally generated levels. In: Proc. 2nd Workshop Artif. Intell. Game Design Process, pp. 41–47 (2013)Google Scholar
  10. 10.
    Hartsook, K., Zook, A., Das, S., Riedl, M.O.: Toward supporting stories with procedurally generated game worlds. In: 2011 IEEE Conference on Computational Intelligence and Games, CIG (2011)Google Scholar
  11. 11.
    Wolfram, S.: Statistical mechanics of cellular automata. Reviews of Modern Physics 601-644 (1983)Google Scholar
  12. 12.
    Mitchell, M., Crutchfield, J.P., Das, R.: Evolving cellular automata with genetic algorithms: A review of recent work. In: Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EvCA 1996) (1996)Google Scholar
  13. 13.
  14. 14.
    Russ, J.C.: The Image Processing Handbook, pp. 777 (2006) Google Scholar
  15. 15.
    De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems, pp. 266. University of Michigan (1975)Google Scholar
  16. 16.
    Bäch, T.: Optimal Mutation Rates in Genetic Search. In: 5th International Conference on Genetic Algorithms, pp. 2–8. Morgan Kaufmann Publishers Inc. (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrew Pech
    • 1
  • Philip Hingston
    • 1
  • Martin Masek
    • 1
  • Chiou Peng Lam
    • 1
  1. 1.School of Computer and Security ScienceEdith Cowan UniversityAustralia

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