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Bacterial Evolution Algorithm for rapid adaptation

  • Emergence
  • Conference paper
  • First Online:
Agents Breaking Away (MAAMAW 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1038))

  • 160 Accesses

Abstract

In this paper, we propose a Bacterial Evolution Algorithm (BEA), inspired by the mechanism of bacteria rapidly adapting themselves to an ever-changing environment. In this paper, we call adaptive agents bacteroids. Bacteroids have their own fitness function that reflects the rates of energy replenishment and collision avoidance. The characteristic of this algorithm is that a selection of bacteroids is made by their environment with their death as a trigger of the selection. This selection is, in general, done irrespective of the bacteroids own fitness function. Even if they have a higher fitness value at a given moment, they will die when they are exposed to a sudden severe environmental condition. If some bacteroids die, the strongest adjacent bacteroids will take over their bodies by inserting their chromosomes, which should be most adaptive in that local area. Mutation is applied at the moment of this takeover to give the bacteroids a chance at evolution. The BEA is appropriate in an environment where many agents like bacteroids are working together in one place with many chances of interaction.

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References

  1. Douglas J. Futuyma. Evolutionary Biology (2nd ed.). Siuauer Associates, Inc., 1986.

    Google Scholar 

  2. David E. Goldberg. Genertic Algorithm, — in Search, Optimization & Machine Learning. Addison Wesley, 1989.

    Google Scholar 

  3. Lynn Margulis and Dorion Sagan. Microcosmos. A Touchstone Book, 1986.

    Google Scholar 

  4. Chisato Numaoka. Blind Hunger Dilemma: An Emergent Collective Behavior from Conflicts. In Proceedings of From. Perception to Action. Conference. IEEE Computer Society Press, September 1994.

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  5. Chisato Numaoka. Introducing Blind Hunger Dilemma: Agents'Properties and Performance. In Proceedings of the First International Conference on Multiagent Systems (ICMAS'95). AAAI Press, June 1995.

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  6. Chisato Numaoka. Symbiosis and Co-evolution in Animats. In Proceedings of the Third European Conference on Artificial Life (ECAL'95). The Springer-Verlag, June 1995.

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Author information

Authors and Affiliations

  1. Sony Computer Science Laboratory Inc., Takanawa Muse Building 3-14-13 Higashi-gotanda, Shinagawa-ku, 141, Tokyo, Japan

    Chisato Numaoka

Authors
  1. Chisato Numaoka
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Editor information

Walter Van de Velde John W. Perram

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© 1996 Springer-Verlag Berlin Heidelberg

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Numaoka, C. (1996). Bacterial Evolution Algorithm for rapid adaptation. In: Van de Velde, W., Perram, J.W. (eds) Agents Breaking Away. MAAMAW 1996. Lecture Notes in Computer Science, vol 1038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0031852

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  • DOI: https://doi.org/10.1007/BFb0031852

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60852-3

  • Online ISBN: 978-3-540-49621-2

  • eBook Packages: Springer Book Archive

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