Mutual information analyses of chaotic neurodynamics driven by neuron selection methods in synchronous exponential chaotic tabu search for quadratic assignment problems

Tetsuo Kawamura, Yoshihiko Horio, Mikio Hasegawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The exponentially decaying tabu search, which exhibits high performance in solving quadratic assignment problems (QAPs), has been implemented on a neural network with chaotic neurodynamics. To exploit the inherent parallel processing capability of analog hardware systems, a synchronous updating scheme, in which all neurons in the network are updated simultaneously, has also been proposed. However, several neurons may fire simultaneously with the synchronous updating. As a result, we cannot determine only one candidate for the 2-opt exchange from among the many fired neurons. To solve this problem, several neuron selection methods, which select a specific neuron from among the fired neurons, have been devised. These neuron selection methods improved the performance of the synchronous updating scheme; however, the dynamics of the chaotic neural network driven by these heuristic algorithms cannot be intuitively understood. In this paper, we analyze the dynamics of a chaotic neural network driven by the neuron selection methods by considering the spatial and temporal mutual information.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publicationTheory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
Pages49-57
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 2010 Nov 222010 Nov 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Neural Information Processing, ICONIP 2010
CountryAustralia
CitySydney, NSW
Period10/11/2210/11/25

Keywords

  • QAP
  • chaotic neural network
  • high-dimensional chaotic dynamics
  • mutual information
  • tabu search

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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