Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms
文献类型：期刊论文
| 作者 | Dong, Weishan1; Yao, Xin2 |
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2008-08-01 |
| 卷号 | 178期号:15页码:3000-3023 |
| 关键词 | estimation of distribution algorithm eigen analysis multivariate Gaussian distribution covariance matrix scaling eigenvalue tuning |
| 英文摘要 | Multivariate Gaussian models are widely adopted in continuous estimation of distribution algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for multivariate Gaussian based EDAs (MGEDAs), named eigen decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen analysis of the covariance matrix, and it explicitly tunes the eigenvalues. All existing MGEDAs can be unified within our ED-EDA framework by applying three different eigenvalue tuning strategies. The effects of eigenvalue on influencing the evolution are investigated through combining maximum likelihood estimates of Gaussian model with each of the eigenvalue tuning strategies in ED-EDA. In our experiments, proper eigenvalue tunings show high efficiency in solving problems with small population sizes, which are difficult for classical MGEDA adopting maximum likelihood estimates alone. Previously developed covariance matrix repairing (CMR) methods focusing on repairing computational errors of covariance matrix can be seen as a special eigenvalue tuning strategy. By using the ED-EDA framework, the computational time of CMR methods can be reduced from cubic to linear. Two new efficient CMR methods are proposed. Through explicitly tuning eigenvalues, ED-EDA provides a new approach to develop more efficient Gaussian based EDAs. (c) 2008 Elsevier Inc. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | OPTIMIZATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000257404100003 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9656]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China 2.Univ Birmingham, Sch Comp Sci, CERCIA, Birmingham B15 2TT, W Midlands, England |
| 推荐引用方式 GB/T 7714 | Dong, Weishan,Yao, Xin. Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms[J]. INFORMATION SCIENCES,2008,178(15):3000-3023. |
| APA | Dong, Weishan,&Yao, Xin.(2008).Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms.INFORMATION SCIENCES,178(15),3000-3023. |
| MLA | Dong, Weishan,et al."Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms".INFORMATION SCIENCES 178.15(2008):3000-3023. |
入库方式： OAI收割
来源：自动化研究所
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