IEICE TRANSACTIONS on Fundamentals
Asymmetric Neural Network and Its Application to Knapsack Problem
Summary :
We propose an asymmetric neural network which can solve inequality-constrained combinatorial optimization problems that are difficult to solve using symmetric neural networks. In this article, a knapsack problem that is one of such the problem is solved using the proposed network. Additionally, we study condition for obtaining a valid solution. In computer simulations, we show that the condition is correct and that the proposed network produces better solutions than the simple greedy algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.3 pp.300-305
- Publication Date
- 1995/03/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section of Selected Papers from the 7th Karuizawa Workshop on Circuits and Systems)
- Category
- Neural Networks
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Akira YAMAMOTO, Masaya OHTA, Hiroshi UEDA, Akio OGIHARA, Kunio FUKUNAGA, "Asymmetric Neural Network and Its Application to Knapsack Problem" in IEICE TRANSACTIONS on Fundamentals,
vol. E78-A, no. 3, pp. 300-305, March 1995, doi: .
Abstract: We propose an asymmetric neural network which can solve inequality-constrained combinatorial optimization problems that are difficult to solve using symmetric neural networks. In this article, a knapsack problem that is one of such the problem is solved using the proposed network. Additionally, we study condition for obtaining a valid solution. In computer simulations, we show that the condition is correct and that the proposed network produces better solutions than the simple greedy algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_3_300/_p
Copy
@ARTICLE{e78-a_3_300,
author={Akira YAMAMOTO, Masaya OHTA, Hiroshi UEDA, Akio OGIHARA, Kunio FUKUNAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Asymmetric Neural Network and Its Application to Knapsack Problem},
year={1995},
volume={E78-A},
number={3},
pages={300-305},
abstract={We propose an asymmetric neural network which can solve inequality-constrained combinatorial optimization problems that are difficult to solve using symmetric neural networks. In this article, a knapsack problem that is one of such the problem is solved using the proposed network. Additionally, we study condition for obtaining a valid solution. In computer simulations, we show that the condition is correct and that the proposed network produces better solutions than the simple greedy algorithm.},
keywords={},
doi={},
ISSN={},
month={March},}
Copy
TY - JOUR
TI - Asymmetric Neural Network and Its Application to Knapsack Problem
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 300
EP - 305
AU - Akira YAMAMOTO
AU - Masaya OHTA
AU - Hiroshi UEDA
AU - Akio OGIHARA
AU - Kunio FUKUNAGA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 1995
AB - We propose an asymmetric neural network which can solve inequality-constrained combinatorial optimization problems that are difficult to solve using symmetric neural networks. In this article, a knapsack problem that is one of such the problem is solved using the proposed network. Additionally, we study condition for obtaining a valid solution. In computer simulations, we show that the condition is correct and that the proposed network produces better solutions than the simple greedy algorithm.
ER -
English
