Modeling Artificial Life: A Cellular Automata Approach

  • Kunjam Nageswara RaoEmail author
  • Madugula Divya
  • M. Pallavi
  • B. Naga Priyanka
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


The key feature of artificial life is the idea of emergence, where new patterns or behaviors emerge from complex computational processes that cannot be predicted. Emergence initiates the formation of higher-order properties via the interaction of lower-level properties. Biological networks contain many theory models of evolution. Similarities between the theoretically estimated networks and empirically modeled counterpart networks are considered as evidence of the theoretic and predictive biological evolution. However, the methods by which these theoretical models are parameterized and modeled might lead to inference validity questions. Opting for randomized parametric values is a probabilistic concern that a model produces. There persists a wide range of probable parameter values which allow a model to produce varying statistic results according to the parameters selected. While using the phenomenon of cellular automata, we tried to model life on a grid of squares. Each square in the grid is taken as a biological cell; we have framed rules such that the process of cell division and pattern formation in terms of biological theoretic perspective is studied. Relatively complex behaviors of the cell patterns which vary from generation to generation are visually analyzed. Three algorithms—game of life, Langton’s ant, and hodgepodge—have been implemented whose technical implementation will provide an inspiration and foundation to build simulators that exhibit characteristics and behaviors of biological systems of reproduction.


Evolution Natural selection Artificial life Modeling life Artificial ecosystem Cellular automata Game of life Langton’s ant Hodgepodge 



We would like to express profound gratitude to Sri Kunjam Nageswara Rao, for his guidance, supervision, and generosity all through the study. We pay equal debt of gratitude to Professor P. Srinivasa Rao, Head of the Department, for providing invariable support and facilities. We are greatly thankful to the other faculty members of the department for their constant encouragement and valuable suggestions. We also thank S. Vakkalanka sir, Asst. Prof. Avanthi Institute of Engineering and Technology, for his suggestions while framing the paper.


  1. 1.
    Bentley PJ (ed) (1999) Evolutionary design by computers. Morgan Kaufmann, Los AltoszbMATHGoogle Scholar
  2. 2.
    Web Link to Future Learn (Creative coding: Monash University).
  3. 3.
    Driessens E, Verstappen M Ima traveller, website
  4. 4.
    Web Link to Online Visualization insights.
  5. 5.
    Haru JI, Graham W (2014) Artificial nature, 14 Aug 2014.
  6. 6.
    Cormack JM (2003) Evolving sonic ecosystems. In: Adamatzky A (ed) The international journal of systems and cybernetics—kybernetes, vol 32 no. 1/2. Emerald, NorthamptonGoogle Scholar
  7. 7.
    Cormack JM (2001) Eden: an evolutionary sonic ecosystem. In: Kelemen J, Sosik P (eds) ECAL 2001. LNCS, vol 2159, pp 133–142. Springer, HeidelbergGoogle Scholar
  8. 8.
    Whitelaw M (2004) Metacreation: art and artificial life. MIT Press, CambridgeGoogle Scholar
  9. 9.
    Hayles NK (1999) How to become Posthuman: virtual bodies in cybernetics, literature and informatics. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  10. 10.
    Dawkins R (1996) The blind watchmaker. W.W. Norton & Company Inc., New YorkGoogle Scholar
  11. 11.
    Sims K (1991) Artificial evolution for computer graphics. In: Proceedings of SIGGRAPH91 computer graphics annual conference series. ACM SIGGRAPH, Las Vegas, New YorkGoogle Scholar
  12. 12.
    Todd S, Latham W (1999) The mutation and growth of art by computers. In: Bentley PJ (ed) Evolutionary design by computers. Morgan Kaufmann, Los Altos, pp 221–250Google Scholar
  13. 13.
    Daniel S Web link to nature of code.
  14. 14.
    Rafael PS, Winfer CT, William ESY (2002) Parallel implementations of cellular automata algorithms on the AGILA high performance computing systems. In: Proceedings of the sixth international symposium on parallel architectures, algorithms, and networks (I-SPAN’02). IEEE Computer Society Press, USA, pp 125–131Google Scholar
  15. 15.
    Reiter C (2009) With J: the Hodge Podge machine, vol 130(41)Google Scholar
  16. 16.
    Jaime S, Eduardo RM Algorithmic sound composition using coupled cellular automata. Interdisciplinary Center for Computer Music Research (ICCMR), University of Plymouth, UKGoogle Scholar
  17. 17.
    Adamatzky A (ed) (2010) Game of life cellular automata. Springer, LondonGoogle Scholar
  18. 18.
    Lichtenegger K (2005) Stochastic cellular automaton models in disease spreading and ecologyGoogle Scholar
  19. 19.
    Jean PB (2001) How fast does Langton’s ant move? J Stat Phys 102:355–360CrossRefzbMATHGoogle Scholar
  20. 20.
    Tatsuie T, Takeo H (2011) Recognizing repeatable configurations of time reversible generalized Langtons Ant is PSPACE—Hard. Algorithms 4(1):1–15Google Scholar
  21. 21.
    Gajardo A, Moreira A, Goles E Complexity of Langton’s Ant. Discrete Appl Math 117(1):41–50Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Kunjam Nageswara Rao
    • 1
    Email author
  • Madugula Divya
    • 1
  • M. Pallavi
    • 1
  • B. Naga Priyanka
    • 1
  1. 1.Department of Computer Science and Systems EngineeringAndhra UniversityVisakhapatnamIndia

Personalised recommendations