| ID | 30033 |
| フルテキストURL | |
| 著者 |
Katai, Osamu
Kyoto University
|
| 抄録 | Estimation of Bayesian network algorithms, which adopt Bayesian networks as the probabilistic model were one of the most sophisticated algorithms in the estimation of distribution algorithms. However the estimation of Bayesian network is key topic of this algorithm, conventional EBNAs adopt greedy searches to search for better network structures. In this paper, we propose a new EBNA, which adopts genetic algorithm to search the structure of Bayesian network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method. |
| キーワード | belief networks
computational complexity
distributed algorithms
genetic algorithms
knapsack problems
probability
|
| 備考 | Digital Object Identifier: 10.1109/ICNNSP.2003.1279302
Published with permission from the copyright holder. This is the institute's copy, as published in Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on, 14-17 Dec. 2003, Vol. 1, Pages 436-439. Publisher URL:http://dx.doi.org/10.1109/ICNNSP.2003.1279302 Copyright © 2003 IEEE. All rights reserved. |
| 発行日 | 2003-12
|
| 出版物タイトル |
Neural Networks and Signal Processing
|
| 巻 | 1巻
|
| 開始ページ | 436
|
| 終了ページ | 439
|
| 資料タイプ |
学術雑誌論文
|
| 言語 |
英語
|
| 査読 |
有り
|
| DOI | |
| Submission Path | industrial_engineering/25
|
