[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Evolutionary Computation for Intelligent Agents Based on Chaotic Retrieval and Soft DNA

  • Conference paper
  • First Online:
Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

Included in the following conference series:

Abstract

This paper proposes a chaotic evolutionary computation algorithm instead of conventional GA (Genetic Algorithm) for such intelligent agents as welfare robots which assist humans. This evolutionary computation is realized by applying chaotic retrieval and Soft DNA(Soft computing oriented Data driven fuNctional scheduling Architecture) on associative memories. We apply this evolutionary computation to multi- agent robots which move abreast and ITS(Intelligent Transport System). Essentially, the process of this evolutionary computation is parallel processing. Therefore, we implement its parallel processing algorithm on A-NET (Actors NETwork) parallel object-oriented computer, and show the usefulness of parallel processing for proposed evolutionary computation.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. T. Hoshino: Dream and Distress of Alife, SHOUKABOU (1994) (in Japanese).

    Google Scholar 

  2. H. Nakahara and T. Sagawa: Virus theory of evolution, HAYAKAWASYOBOU (1996) (in Japanese).

    Google Scholar 

  3. W.-Z. Xu, A. Kashiwagi, T. Yomo and I. Urabe: Fate of a mutant emerging at the initial stage of evolution, Researches in Population Ecology, 38(2), pp231–237 (1996).

    Article  Google Scholar 

  4. N. Kohata, T. Yamaguchi, Y. Wakamatsu and T. Baba: Evolutionary Parallel Computation based on Chaotic Retrieval and Creation, Proceedings of the 4th International Conference on Soft Computing (IIZUKA’96), Vol.2, pp.638–641 (1996).

    Google Scholar 

  5. T. Yamaguchi: Fuzzy Associative Memory System, Journal of Japan Society for Fuzzy Theory and Systems, Vol.5,No.2, 245–260 (1993) (in Japanese).

    Google Scholar 

  6. J. Tani: Proposal of Chaotic Steepest Descent Method for Neural Networks and Analysis of Their Dynamics, Trans.IEICE, Vol. J74-A,No.8, pp1208–1215 (1991).

    Google Scholar 

  7. B. Kosko: Adaptive Bidirectional Associative Memories, Applied Optic, Vol.26,No.23, pp.4947–4960 (1987).

    Article  Google Scholar 

  8. T. Baba, T. Yoshinaga, Y. Iwamoto and D. Abe: The A-NET Working Prototype: A Parallel Object-Oriented Multicomputer with Reconfigurable Network, Proc. Int. Workshop on Innovative Architecture for Future Generation High-Performance Processors and Systems, IEEE Computer Society Press, pp.40–49 (1998).

    Google Scholar 

  9. T. Takagi, T. Yamaguchi and M. Sugeno: Conceptual Fuzzy Sets, International Fuzzy Engineering Symposium’91 (IFES’91), Vol.2, pp.261–272 (1991).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Science, Faculty of Engineering, Utsunomiya University, 7-1-2 Youtou, Utsunomiya, 321-8585, Japan

    Naoki Kohata, Makoto Sato, Toru Yamaguchi & Takanobu Baba

  2. Institute of Industrial Science, University of Tokyo, 7-22-1 Roppongi, Minato-ku, Tokyo, 106-8558, Japan

    Hideki Hashimoto

Authors
  1. Naoki Kohata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Makoto Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Toru Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takanobu Baba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hideki Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra, ACT, Australia, 2600

    Bob McKay , Xin Yao  & Charles S. Newton ,  & 

  2. Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1, Kusung-dong, Yusung-gu, Taejon-shi, 305-701, Korea

    Jong-Hwan Kim

  3. Department of Information Electronics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Takeshi Furuhashi

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kohata, N., Sato, M., Yamaguchi, T., Baba, T., Hashimoto, H. (1999). Evolutionary Computation for Intelligent Agents Based on Chaotic Retrieval and Soft DNA. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_33

Download citation

  • DOI: https://doi.org/10.1007/3-540-48873-1_33

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation