Case study of solving optimization problems using neural networks

X. Xu; S. Chen; W. T. Tsai; N. K. Huang

doi:10.1016/0893-6080(88)90190-6

Case study of solving optimization problems using neural networks

X. Xu, S. Chen, W. T. Tsai, N. K. Huang

Computing and Augmented Intelligence, School of (IAFSE-SCAI)

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper studies the performance of Hopfield's neural network also known as the McCulloch-Pitts model, in solving optimization problems. We have discovered that it can not solve many problems satisfactorily including the Traveling Salesman Problem (TSP). But, it can solve some problems quite well, e.g., the Graph Coloring Problem (GCP). Near-optimal solutions can be easily obtained using our methodology, and the performances, contrary to common belief, are not so much affected by the parameters of the energy function, if the parameters are within certain range.

Original language	English (US)
Pages (from-to)	151
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90190-6
State	Published - 1988
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: Sep 6 1988 → Sep 10 1988

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

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10.1016/0893-6080(88)90190-6

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title = "Case study of solving optimization problems using neural networks",

abstract = "This paper studies the performance of Hopfield's neural network also known as the McCulloch-Pitts model, in solving optimization problems. We have discovered that it can not solve many problems satisfactorily including the Traveling Salesman Problem (TSP). But, it can solve some problems quite well, e.g., the Graph Coloring Problem (GCP). Near-optimal solutions can be easily obtained using our methodology, and the performances, contrary to common belief, are not so much affected by the parameters of the energy function, if the parameters are within certain range.",

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AU - Xu, X.

AU - Chen, S.

AU - Tsai, W. T.

AU - Huang, N. K.

PY - 1988

Y1 - 1988

N2 - This paper studies the performance of Hopfield's neural network also known as the McCulloch-Pitts model, in solving optimization problems. We have discovered that it can not solve many problems satisfactorily including the Traveling Salesman Problem (TSP). But, it can solve some problems quite well, e.g., the Graph Coloring Problem (GCP). Near-optimal solutions can be easily obtained using our methodology, and the performances, contrary to common belief, are not so much affected by the parameters of the energy function, if the parameters are within certain range.

AB - This paper studies the performance of Hopfield's neural network also known as the McCulloch-Pitts model, in solving optimization problems. We have discovered that it can not solve many problems satisfactorily including the Traveling Salesman Problem (TSP). But, it can solve some problems quite well, e.g., the Graph Coloring Problem (GCP). Near-optimal solutions can be easily obtained using our methodology, and the performances, contrary to common belief, are not so much affected by the parameters of the energy function, if the parameters are within certain range.

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AN - SCOPUS:0024176103

SN - 0893-6080

VL - 1

SP - 151

JO - Neural Networks

JF - Neural Networks

IS - 1 SUPPL

T2 - International Neural Network Society 1988 First Annual Meeting

Y2 - 6 September 1988 through 10 September 1988

ER -

Case study of solving optimization problems using neural networks

Abstract

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