Skip to main content

Effectively Evolving Finite State Machines Compared to Enumeration

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6927)

Abstract

We want to answer the question how effectively finite state machines (FSMs) controlling the behavior of local agents in a multi agent system with a global task can be evolved by a genetic algorithm (GA). Different variants of the GA were used by varying the mutation techniques and the population size. In order to evaluate the effectiveness of the GA the optimal behavior is used for comparison. This optimal behavior can be found by a sophisticated enumeration technique. The agents’ global task is to explore an unknown area with obstacles. The number of states of the controlling FSM was restricted to five in order to keep the computation time for the enumeration acceptable. The results show that the GA is reliable and almost as effective as enumeration while being significantly faster.

Keywords

  • Genetic Algorithm
  • State Machine
  • Cellular Automaton
  • Multi Agent System
  • Finite State Machine

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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. Halbach, M., Heenes, W., Hoffmann, R., Tisje, J.: Optimizing the behavior of a moving creature in software and in hardware. Springer, Heidelberg (2004)

    CrossRef  MATH  Google Scholar 

  2. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  3. Halbach, M.: Algorithmen und Hardwarearchitekturen zur optimierten Aufzählung von Automaten und deren Einsatz bei der Simulation künstlicher Kreaturen. PhD thesis, Technische Universität Darmstadt (2008)

    Google Scholar 

  4. Komann, M., Ediger, P., Fey, D., Hoffmann, R.: On the Effectivity of Genetic Programming Compared to the Time-Consuming Full Search of Optimal 6-State Automata. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 280–291. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  5. Ediger, P., Hoffmann, R., Halbach, M.: Evolving 6-state Automata for Optimal Behaviors of Creatures Compared to Exhaustive Search. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 689–696. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  6. Komann, M., Fey, D.: Evaluating the Evolvability of Emergent Agents with Different Numbers of States

    Google Scholar 

  7. Di Stefano, B.N., Lawniczak, A.T.: Autonomous roving object’s coverage of its universe. In: CCECE, pp. 1591–1594. IEEE, Los Alamitos (2006)

    Google Scholar 

  8. Halbach, M., Hoffmann, R., Both, L.: Optimal 6-state algorithms for the behavior of several moving creatures. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 571–581. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ediger, P., Hoffmann, R., Grüner, S. (2012). Effectively Evolving Finite State Machines Compared to Enumeration. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27549-4_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27548-7

  • Online ISBN: 978-3-642-27549-4

  • eBook Packages: Computer ScienceComputer Science (R0)