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Combinatorial Optimization by Amoeba-Based Neurocomputer with Chaotic Dynamics

  • Masashi Aono
  • Yoshito Hirata
  • Masahiko Hara
  • Kazuyuki Aihara
Part of the Proceedings in Information and Communications Technology book series (PICT, volume 1)

Abstract

We demonstrate a computing system based on an amoeba of a true slime mold Physarum capable of producing rich spatiotemporal oscillatory behavior. Our system operates as a neurocomputer because an optical feedback control in accordance with a recurrent neural network algorithm leads the amoeba’s photosensitive branches to search for a stable configuration concurrently. We show our system’s capability of solving the traveling salesman problem. Furthermore, we apply various types of nonlinear time series analysis to the amoeba’s oscillatory behavior in the problem-solving process. The results suggest that an individual amoeba might be characterized as a set of coupled chaotic oscillators.

Keywords

Travel Salesman Problem Travel Salesman Problem Optical Feedback Serial Dependence Physarum Polycephalum 
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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Copyright information

© Springer Tokyo 2009

Authors and Affiliations

  • Masashi Aono
    • 1
  • Yoshito Hirata
    • 2
    • 3
  • Masahiko Hara
    • 1
  • Kazuyuki Aihara
    • 2
    • 3
  1. 1.Flucto-Order Functions Asian Collaboration Team, RIKEN (The Institute of Physical and Chemical Research), WakoSaitamaJapan
  2. 2.Institute of Industrial ScienceThe University of TokyoTokyoJapan
  3. 3.ERATO Aihara Complexity Modelling Project, JST, Shibuya-kuTokyoJapan

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