期刊论文、会议论文

Hu, Xiaohua

英文

BMC Genomics

1471-2164

2012

13

3

1-11

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

NOV 12-15, 2011

Atlanta, GA

[Hu, Xiaohua] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.^[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.^[Li, Zhoujun] Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China.^[Li, Zhoujun] Beihang Univ, Beijing Key Lab Network Technol, Beijing 100191, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci, Philadelphia, PA 19104 USA.^[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.

IEEE, IEEE Comp Soc

CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

BMC

10.1186/1471-2164-13-S3-S6

This article has been published as part of BMC Genomics Volume 13 Supplement 3, 2012: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/ bmcgenomics/supplements/13/S3. This work was supported by the National Natural Science Foundation of China (60973105, 90718017, 61170189), the Research Fund for the Doctoral Program of Higher Education (20111102130003), the Fund of the State Key Laboratory of Software Development Environment (SKLSDE-2011ZX-03), the Scientific Research Fund of Hunan Provincial Education Department (09A105), the Talents Import Fund of Central South University of Forestry and Technology (104-0177) and the Fund of Hunan Provincial University Library and Information Commission (2011L058).

本校为通讯机构

Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot...