Skip to main content

Species Formation in Evolving Finite State Machines

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1674))

Abstract

Since the early beginnings of Evolutionary Computation, Finite State Machines (FSMs) have been applied to model organisms. We present a new approach to evolve such artificial organisms. The FSMs are subject to a difficult navigation and searching task in heterogeneous environments. We give a definition of FSM-species and investigate their formation. The results show that species are formed as the organisms agree on a common ‘genetic broadcast language’ and take advantage of the fruitful effects of recombination. As observed in natural ecosystems, higher abiotic diversity leads to higher biotic diversity.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York (1996)

    MATH  Google Scholar 

  2. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming-An Introduction: On the Automatic Programming of Computer Programs and its Applications. Morgan Kaufman, San Francisco, dpunkt.verlag, Heidelberg (1998)

    MATH  Google Scholar 

  3. Collins, R.J., Jefferson, D.R.: Representations for Artificial Organisms. In: Meyer, J.-A. (ed.): From Animals to Animats, Proceedings of the First International Conference on Simulation of Adaptive Behavior. MIT Press, Cambridge (1991) 382–390

    Google Scholar 

  4. Collins, R.J., Jefferson, D.R.: AntFarm: Towards Simulated Evolution. In: Langton, C.G., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.): Artificial Life II. Addison Wesley, Redwood City (1991) 579–601

    Google Scholar 

  5. Fogel, D.B.: Evolving Behaviors in the Iterated Prisoner’s Dilemma. Evol. Comp. 1 (1993) 77–97

    Google Scholar 

  6. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. John Wiley, New York (1966)

    MATH  Google Scholar 

  7. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  8. Hopcroft, J.E., Ullmann, J.D.: Introduction to Automata Theory, Languages and Computation. Addison-Wesley, Reading (1979)

    MATH  Google Scholar 

  9. Jefferson, D.R., Collins, R.J., Cooper, C., Dyer, M., Flowers, M., Korf, R., Taylor, C., Wang, A.: Evolution as a Theme in Artificial Life: The Genesys/Tracker System. In: Langton, C.G., Taylor, C, Farmer, J.D., Rasmussen, S. (eds.): Artificial Life II. Addison-Wesley, Redwood City (1991) 549–578

    Google Scholar 

  10. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer-Verlag, Berlin Heidelberg New York (1996)

    MATH  Google Scholar 

  11. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

  12. Schwefel, H.-P.: Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie: Mit einer vergleichenden Einführung in die Hill-Climbing und Zufallsstrategien. Birkhäuser, Basel (1977)

    Google Scholar 

  13. Törn, A., Žilinskas, A.: Global Optimization, Lecture Notes in Computer Science, Vol. 350. Springer-Verlag, Berlin Heidelberg New York (1989)

    MATH  Google Scholar 

  14. Wilson, S.W.: Knowledge Growth in an Artificial Animal. In: Greffenstette, J.J. (ed.), Proceedings of the First International Conference on Genetic Algorithms and Their Applications. Erlbaum, Hillsdale (1985) 16–23

    Google Scholar 

  15. EFSM homepage: http://www.bitoek.uni-bayreuth.de/MOD/Software/EFSM/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rasek, A., Dörwald, W., Hauhs, M., Kastner-Maresch, A. (1999). Species Formation in Evolving Finite State Machines. In: Floreano, D., Nicoud, JD., Mondada, F. (eds) Advances in Artificial Life. ECAL 1999. Lecture Notes in Computer Science(), vol 1674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48304-7_20

Download citation

  • DOI: https://doi.org/10.1007/3-540-48304-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66452-9

  • Online ISBN: 978-3-540-48304-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics