Encyclopedia of Complexity and Systems Science

2009 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Evolving Cellular Automata

  • Martin Cenek
  • Melanie Mitchell
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30440-3_191

Definition of the Subject

Evolving cellular automata refers to the application of evolutionary computation methods to evolve cellular automata transition rules. This hasbeen used as one approach to automatically “programming” cellular automata to perform desired computations, and as an approach to model theevolution of collective behavior in complex systems.

Introduction

In recent years, the theory and application of cellular automata (CAs) has experienced a renaissance, due to advances in the related fields ofreconfigurable hardware, sensor networks, and molecular‐scale computing systems. In particular, architectures similar to CAs can be used toconstruct physical devices such as field configurable gate arrays for electronics, networks of robots for environmental sensing and nano‐devicesembedded in interconnect fabric used for fault tolerant nanoscale computing. Such devices consist of networks of simple components that communicatelocally without centralized control. Two major areas...

This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work has been funded by the Center on Functional Engineered Nano Architectonics (FENA), through the Focus Center Research Program of the Semiconductor Industry Association.

Bibliography

  1. 1.
    Alba E, Giacobini M, Tomassini M, Romero S (2002) Comparing synchronous andasynchronous cellular genetic algorithms. In: Guervos MJJ et al (eds) Parallel problem solving from nature. PPSN VII, Seventh International Conference.Springer, Berlin, pp 601–610Google Scholar
  2. 2.
    Andre D, Bennett FH III, Koza JR (1996) Evolution of intricate long‐distancecommunication signals in cellular automata using genetic programming. In: Artificial life V: Proceedings of the fifth international workshop on thesynthesis and simulation of living systems. MIT Press, CambridgeGoogle Scholar
  3. 3.
    Ashlock D (2006) Evolutionary computation for modeling andoptimization. Springer, New YorkzbMATHGoogle Scholar
  4. 4.
    Back T (1996) Evolutionary algorithms in theory and practice. Oxford UniversityPress, New YorkGoogle Scholar
  5. 5.
    Basanta D, Bentley PJ, Miodownik MA, Holm EA (2004) Evolving cellular automata togrow microstructures. In: Genetic programming: 6th European Conference. EuroGP 2003, Essex, UK, April 14–16, 2003. Proceedings. Springer, Berlin,pp 77–130Google Scholar
  6. 6.
    Bersini H, Detours V (2002) Asynchrony induces stability in cellular automatabased models. In: Proceedings of the IVth conference on artificial life. MIT Press, Cambridge, pp 382–387Google Scholar
  7. 7.
    Bucci A, Pollack JB (2002) Order‐theoretic analysis of coevolutionproblems: Coevolutionary statics. In: GECCO 2002 Workshop on Understanding Coevolution: Theory andAnalysis of Coevolutionary Algorithms, vol 1. Morgan Kaufmann, San Francisco, pp 229–235Google Scholar
  8. 8.
    Burks A (1970) Essays on cellular automata. University of Illinois Press,UrbanzbMATHGoogle Scholar
  9. 9.
    Cartlidge J, Bullock S (2004) Combating coevolutionary disengagement by reducingparasite virulence. Evol Comput 12(2):193–222Google Scholar
  10. 10.
    Chopra P, Bender A (2006) Evolved cellular automata for protein secondarystructure prediction imitate the determinants for folding observed in nature. Silico Biol 7(0007):87–93Google Scholar
  11. 11.
    Codd EF (1968) Cellular automata. ACM Monograph series, New YorkzbMATHGoogle Scholar
  12. 12.
    Corno F, Reorda MS, Squillero G (2000) Exploiting the selfish gene algorithmfor evolving cellular automata. IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00) 06:6577Google Scholar
  13. 13.
    Crutchfield JP, Mitchell M, Das R (2003) The evolutionary design of collectivecomputation in cellular automata. In: Crutchfield JP, Schuster PK (eds) Evolutionary Dynamics – Exploring the Interplay of Selection, Neutrality,Accident, and Function. Oxford University Press, New York, pp 361–411Google Scholar
  14. 14.
    Das R, Crutchfield JP, Mitchell M, Hanson JE (1995) Evolving globallysynchronized cellular automata. In: Eshelman L (ed) Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann, SanFrancisco, pp 336–343Google Scholar
  15. 15.
    Das R, Mitchell M, Crutchfield JP (1994) A genetic algorithm discoversparticle‐based computation in cellular automata. In: Davidor Y, Schwefel HP, Männer R (eds) Parallel Problem Solving fromNature‐III. Springer, Berlin, pp 344–353Google Scholar
  16. 16.
    Farmer JD, Toffoli T, Wolfram S (1984) Cellular automata: Proceedings of aninterdisciplinary workshop. Elsevier Science, Los AlamoszbMATHGoogle Scholar
  17. 17.
    Funes P, Sklar E, Juille H, Pollack J (1998) Animal‐animat coevolution:Using the animal population as fitness function. In: Pfeiffer R, Blumberg B, Wilson JA, Meyer S (eds) From animals to animats 5: Proceedings of the fifthinternational conference on simulation of adaptive behavior. MIT Press, Cambridge, pp 525–533Google Scholar
  18. 18.
    Gardner M (1970) Mathematical games: The fantastic combinations of JohnConway's new solitaire game “Life”. Sci Am 223:120–123Google Scholar
  19. 19.
    Grassberger P (1983) Chaos and diffusion in deterministic cellularautomata. Physica D 10(1–2):52–58MathSciNetADSGoogle Scholar
  20. 20.
    Hanson JE (1993) Computational mechanics of cellular automata. Ph D Thesis,Univeristy of California at BerkeleyGoogle Scholar
  21. 21.
    Hanson JE, Crutchfield JP (1992) The attractor‐basin portrait of a cellular automaton. J Stat Phys 66:1415–1462MathSciNetADSzbMATHGoogle Scholar
  22. 22.
    Hartman H, Vichniac GY (1986) Inhomogeneous cellular automata (inca). In:Bienenstock E, Fogelman F, Weisbuch G (eds) Disordered Systems and Biological Organization, vol F20. Springer, Berlin,pp 53–57Google Scholar
  23. 23.
    Hillis WD (1990) Co‐evolving parasites improve simulated evolution as anoptimization procedure. Physica D 42:228–234ADSGoogle Scholar
  24. 24.
    Hordijk W, Crutchfield JP, Mitchell M (1996) Embedded‐particlecomputation in evolved cellular automata. In: Toffoli T, Biafore M, Leão J (eds) Physics andComputation 1996. New England Complex Systems Institute, Cambridge, pp 153–158Google Scholar
  25. 25.
    Huberman BA, Glance NS (1993) Evolutionary games and computer simulations.Proc Natl Acad Sci 90:7716–7718ADSzbMATHGoogle Scholar
  26. 26.
    Ikebe M, Amemiya Y (2001) VMoS cellular‐automaton circuit for pictureprocessing. In: Miki T (ed) Brainware: Bio‐inspired architectures and its hardware implementation, vol 6 of FLSI Soft Computing, chapter 6. World Scientific, Singapore, pp 135–162Google Scholar
  27. 27.
    Jiménez‐Morales F, Crutchfield JP, Mitchell M (2001) Evolvingtwo‐dimensional cellular automata to perform density classification: A report on work in progress. Parallel Comput27(5):571–585Google Scholar
  28. 28.
    Juillé H, Pollack JB (1998) Coevolutionary learning: A case study. In:Proceedings of the fifteenth international conference on machinelearning (ICML-98). Morgan Kaufmann, San Francisco,pp 24–26Google Scholar
  29. 29.
    Koza JR (1992) Genetic programming: On the programming of computers by means ofnatural selection. MIT Press, CambridgezbMATHGoogle Scholar
  30. 30.
    Koza JR (1994) Genetic programming II: Automatic discovery of reusableprograms. MIT Press, CambridgezbMATHGoogle Scholar
  31. 31.
    Land M, Belew RK (1995) No perfect two-state cellular automata for densityclassification exists. Phys Rev Lett 74(25):5148–5150ADSGoogle Scholar
  32. 32.
    Langton C (1986) Studying artificial life with cellular automata. Physica D10D:120MathSciNetGoogle Scholar
  33. 33.
    Langton C (1990) Computation at the edge of chaos: Phase transitions andemergent computation. Physica D 42:12–37MathSciNetADSGoogle Scholar
  34. 34.
    Lohn JD, Reggia JA (1997) Automatic discovery of self‐replicatingstructures in cellular automata. IEEE Trans Evol Comput 1(3):165–178Google Scholar
  35. 35.
    Madore BF, Freedman WL (1983) Computer simulations of theBelousov‐Zhabotinsky reaction. Science 222:615–616ADSGoogle Scholar
  36. 36.
    Mitchell M (1996) An introduction to genetic algorithms. MIT Press, CambridgeGoogle Scholar
  37. 37.
    Mitchell M (1998) Computation in cellular automata: A selected review. In: Gramss T, Bornholdt S, Gross M, Mitchell M, Pellizzari T (eds) Nonstandard Computation. VCH, Weinheim, pp 95–140Google Scholar
  38. 38.
    Mitchell M, Hraber PT, Crutchfield JP (1993) Revisiting the edge of chaos:Evolving cellular automata to perform computations. Complex Syst 7:89–130zbMATHGoogle Scholar
  39. 39.
    Mitchell M, Thomure MD, Williams NL (2006) The role of space in the success ofcoevolutionary learning. In: Rocha LM, Yaeger LS, Bedau MA, Floreano D, Goldstone RL, Vespignani A (eds) Artificial life X: Proceedings of the tenthinternational conference on the simulation and synthesis of living systems. MIT Press, Cambridge, pp 118–124Google Scholar
  40. 40.
    Packard NH (1988) Adaptation toward the edge of chaos. In: Kelso JAS, MandellAJ, Shlesinger M (eds) Dynamic patterns in complex systems. World Scientific, Singapore, pp 293–301Google Scholar
  41. 41.
    Pagie L, Hogeweg P (1997) Evolutionary consequences of coevolving targets. EvolComput 5(4):401–418Google Scholar
  42. 42.
    Pagie L, Mitchell M (2002) A comparison of evolutionary and coevolutionarysearch. Int J Comput Intell Appl 2(1):53–69Google Scholar
  43. 43.
    Reynaga R, Amthauer E (2003) Two‐dimensional cellular automata of radiusone for density classification task \( { \rho=\frac{1}{2} } \). Pattern Recogn Lett 24(15):2849–2856zbMATHGoogle Scholar
  44. 44.
    Rosin C, Belew R (1997) New methods for competitive coevolution. Evol Comput5(1):1–29Google Scholar
  45. 45.
    Schadschneider A (2001) Cellular automaton approach to pedestriandynamics – theory. In: Pedestrian and evacuation dynamics. Springer, Berlin,pp 75–86Google Scholar
  46. 46.
    Sipper M (1994) Non‐uniform cellular automata: Evolution in rule spaceand formation of complex structures. In: Brooks RA, Maes P (eds) Artificial life IV. MIT Press, Cambridge,pp 394–399Google Scholar
  47. 47.
    Sipper M (1997) Evolution of parallel cellular machines: The cellularprogramming approach. Springer, HeidelbergGoogle Scholar
  48. 48.
    Sipper M, Ruppin E (1997) Co‐evolving architectures for cellularmachines. Physica D 99:428–441zbMATHGoogle Scholar
  49. 49.
    Sipper M, Tomassini M, Capcarrere M (1997) Evolving asynchronous and scalablenon‐uniform cellular automata. In: Proceedings of the international conference on artificial neural networks and genetic algorithms(ICANNGA97). Springer, Vienna, pp 382–387Google Scholar
  50. 50.
    Subrata R, Zomaya AY (2003) Evolving cellular automata for location managementin mobile computing networks. IEEE Trans Parallel Distrib Syst 14(1):13–26Google Scholar
  51. 51.
    Tan SK, Guan SU (2007) Evolving cellular automata to generate nonlinearsequences with desirable properties. Appl Soft Comput 7(3):1131–1134Google Scholar
  52. 52.
    Teuscher C (2006) On irregular interconnect fabrics for self‐assemblednanoscale electronics. In: Tyrrell AM, Haddow PC, Torresen J (eds) 2nd ieee international workshop on defect and fault tolerant nanoscale architectures,NANOARCH'06. Lecture Notes in Computer Science, vol 2602. ACM Press, New York, pp 60–67Google Scholar
  53. 53.
    Teuscher C, Capcarrere MS (2003) On fireflies, cellular systems, andevolware. In: Tyrrell AM, Haddow PC, Torresen J (eds) Evolvable systems: From biology to hardware. Proceedings of the 5th international conference,ICES2003. Lecture Notes in Computer Science, vol 2602. Springer, Berlin, pp 1–12Google Scholar
  54. 54.
    Vichniac GY, Tamayo P, Hartman H (1986) Annealed and quenched inhomogeneouscellular automata. J Stat Phys 45:875–883MathSciNetADSGoogle Scholar
  55. 55.
    von Neumann J (1966) Theory of Self‐ReproducingAutomata. University of Illinois Press, ChampaignGoogle Scholar
  56. 56.
    Wiegand PR, Sarma J (2004) Spatial embedding and loss of gradient incooperative coevolutionary algorithms. Parallel Probl Solving Nat 1:912–921Google Scholar
  57. 57.
    Williams N, Mitchell M (2005) Investigating the success of spatialcoevolution. In: Proceedings of the 2005 conference on genetic and evolutionary computation. Washington DC,pp 523–530Google Scholar
  58. 58.
    Wolfram S (1984) Universality and complexity in cellular automata. Physica D10D:1MathSciNetADSGoogle Scholar
  59. 59.
    Wolfram S (1986) Theory and application of cellular automata. World ScientificPublishing, SingaporeGoogle Scholar
  60. 60.
    Wolfram S (2002) A new kind of science. Wolfram Media, ChampaignzbMATHGoogle Scholar
  61. 61.
    Yu T, Lee S (2002) Evolving cellular automata to model fluid flow in porousmedia. In: 2002 Nasa/DoD conference on evolvable hardware (EH '02). IEEE Computer Society, Los Alamitos, pp 210Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Martin Cenek
    • 1
  • Melanie Mitchell
    • 1
  1. 1.Computer Science DepartmentPortland State UniversityPortlandUSA