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A Survey of Heuristic Search Method of Multimodal Optimum Point

  • Moriya Oda
  • Kahei Nakamura
  • B. F. Womack

Abstract

This is a survey of a theoretical and experimental study of a heuristic method of searching the globally optimum point of a multimodal, two-dimensional, nonlinear, and unknown criterion function by using the sighted, blindfolded, and blind subjects. First, the paper defines a heuristic search, establishes an experimental method for such a heuristic search, and develops a series of heuristic search experiments. Second, the paper shows some examples of the heuristic search behavior. Five kinds of the heuristic search model are extracted through five kinds of data processing based on a mode analysis. Some extracted models are proven to be appropriate and average. Third, the process of the heuristic search is studied from a viewpoint of concept formation by using a new idea of concept matrix.

Keywords

Heuristic Search Concept Formation Trial Number Trial Point Blind Subject 
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.

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References

  1. (la).
    M. Oda and K. Kakamura; “Heuristic Method of Searching an Optimum Point of a Two-Dimensional Multimodal Criterion Function”, J. of Society of Instrument and Control Engineers of Japan, 7–12, 16/22 (Dec. 1968).Google Scholar
  2. (lb).
    Also available in English, Res. Rept. of Automatic Control Lab., Nagoya Univ., Nagoya, Japan, 16,1/10 (Aprl. 1969).Google Scholar
  3. (2).
    M. Oda and B.F. Womack; “Experimental Study of Heuristic Search of Multimodal Optimum Point by Human Subjects”, Proceedings of 3rd Houston Conf. on Computer and System Sciences, Houston, Texas, 689/700 (Apr. 26–27, 1971).Google Scholar
  4. (3).
    M. Oda and B.F. Womack; “Experimental Study of Heuristic Search Method for Two-Dimensional Multimodal Optimum Point”, Res. Rept. of Automatic Control Lab., Nagoya Univ., Nagoya, Japan, 19, 15/34 (June 1972).Google Scholar
  5. (4).
    M. Oda, B.F. Womack, and K. Nakamura; “Heuristic Search Behavior by Blind Subject”, Conf. of Automatic Control, Inst. of Ele. Engrs. of Japan (may 30, 1972).Google Scholar
  6. (5).
    M. Oda, T. Nagaoka, and K. Nakamura; “Heuristic Search and Concept Formation”, 12th Annual Conf. of Soc. of Instrument and Control Engrs., Japan, 367/374 (Aug. 1973).Google Scholar
  7. (6).
    J. Opacić “A Heuristic Method for Finding Most Extrema of a Non-linear Function”, IEEE Trans., SMC-3–1, 102/107 (Jan. 1973).Google Scholar
  8. (7).
    S. Harinasuta; “Heuristics for Search Techniques”, M.S. Thesis, Dept. of Ele. Eng., Univ. of Texas at Austin, Austin, Texas, U.S.A. (Dec. 1971).Google Scholar
  9. (8).
    R.H. Graham; “Basic Pattern in Heuristic Searches”, M.S. Thesis, Dept. of Ele. Eng., Univ. of Texas at Austin, Austin, Texas, U.S.A. (Feb. 1972).Google Scholar
  10. (9).
    M. Oda and B.F. Womack; “A Unified Discussion on Heuristics in Artificial Intelligence”, Proceedings of Third Asilomar Conf., 387/400 (Dec. 1969).Google Scholar
  11. (10).
    M. Oda and B.F. Womack; “A Detailed Investigation of Various Heuristic Approaches in Artificial Intelligence,” 1970 SWIEEECO Record, 125/129 (Apr. 1970).Google Scholar
  12. (11).
    M. Oda and B.F. Womack; “New Definition and General Discussion on Heuristics”, invited paper at 1971 JACC, St. Louis, Mo., 3/5 (Aug. 1971).Google Scholar
  13. (12).
    M. Oda and K. Nakamura; “Concept Formation in Heuristic Search Behavior”, 1st International Joint Conf. on Pattern Recognition, Washington, D.C. (Oct. 30 — Nov. 1, 1973).Google Scholar

Copyright information

© Plenum Press, New York 1974

Authors and Affiliations

  • Moriya Oda
    • 1
  • Kahei Nakamura
    • 1
  • B. F. Womack
    • 2
  1. 1.Nagoya UniversityNagoyaJapan
  2. 2.The University of TexasAustinUSA

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