IEICE TRANSACTIONS on Fundamentals
An Analysis on Additive Effects of Nonlinear Dynamics for Combinatorial Optimization
Summary :
We analyze additive effects of nonlinear dynamics for conbinatorial optimization. We apply chaotic time series as noise sequence to neural networks for 10-city and 20-city traveling salesman problems and compare the performance with stochastic processes, such as Gaussian random numbers, uniform random numbers, 1/fα noise and surrogate data sets which preserve several statistics of the original chaotic data. In result, it is shown that not only chaotic noise but also surrogates with similar autocorrelation as chaotic noise exhibit high solving abilities. It is also suggested that since temporal structure of chaotic noise characterized by autocorrelation affects abilities for combinatorial optimization problems, effects of chaotic sequence as additive noise for escaping from undesirable local minima in case of solving combinatorial optimization problems can be replaced by stochastic noise with similar autocorrelation.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.1 pp.206-213
- Publication Date
- 1997/01/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Neural Networks
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Mikio HASEGAWA, Tohru IKEGUCHI, Takeshi MATOZAKI, Kazuyuki AIHARA, "An Analysis on Additive Effects of Nonlinear Dynamics for Combinatorial Optimization" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 1, pp. 206-213, January 1997, doi: .
Abstract: We analyze additive effects of nonlinear dynamics for conbinatorial optimization. We apply chaotic time series as noise sequence to neural networks for 10-city and 20-city traveling salesman problems and compare the performance with stochastic processes, such as Gaussian random numbers, uniform random numbers, 1/fα noise and surrogate data sets which preserve several statistics of the original chaotic data. In result, it is shown that not only chaotic noise but also surrogates with similar autocorrelation as chaotic noise exhibit high solving abilities. It is also suggested that since temporal structure of chaotic noise characterized by autocorrelation affects abilities for combinatorial optimization problems, effects of chaotic sequence as additive noise for escaping from undesirable local minima in case of solving combinatorial optimization problems can be replaced by stochastic noise with similar autocorrelation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_1_206/_p
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@ARTICLE{e80-a_1_206,
author={Mikio HASEGAWA, Tohru IKEGUCHI, Takeshi MATOZAKI, Kazuyuki AIHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Analysis on Additive Effects of Nonlinear Dynamics for Combinatorial Optimization},
year={1997},
volume={E80-A},
number={1},
pages={206-213},
abstract={We analyze additive effects of nonlinear dynamics for conbinatorial optimization. We apply chaotic time series as noise sequence to neural networks for 10-city and 20-city traveling salesman problems and compare the performance with stochastic processes, such as Gaussian random numbers, uniform random numbers, 1/fα noise and surrogate data sets which preserve several statistics of the original chaotic data. In result, it is shown that not only chaotic noise but also surrogates with similar autocorrelation as chaotic noise exhibit high solving abilities. It is also suggested that since temporal structure of chaotic noise characterized by autocorrelation affects abilities for combinatorial optimization problems, effects of chaotic sequence as additive noise for escaping from undesirable local minima in case of solving combinatorial optimization problems can be replaced by stochastic noise with similar autocorrelation.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - An Analysis on Additive Effects of Nonlinear Dynamics for Combinatorial Optimization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 206
EP - 213
AU - Mikio HASEGAWA
AU - Tohru IKEGUCHI
AU - Takeshi MATOZAKI
AU - Kazuyuki AIHARA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 1997
AB - We analyze additive effects of nonlinear dynamics for conbinatorial optimization. We apply chaotic time series as noise sequence to neural networks for 10-city and 20-city traveling salesman problems and compare the performance with stochastic processes, such as Gaussian random numbers, uniform random numbers, 1/fα noise and surrogate data sets which preserve several statistics of the original chaotic data. In result, it is shown that not only chaotic noise but also surrogates with similar autocorrelation as chaotic noise exhibit high solving abilities. It is also suggested that since temporal structure of chaotic noise characterized by autocorrelation affects abilities for combinatorial optimization problems, effects of chaotic sequence as additive noise for escaping from undesirable local minima in case of solving combinatorial optimization problems can be replaced by stochastic noise with similar autocorrelation.
ER -
English
