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15篇 您的检索式：作者名="Sun Tingkai"

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		题名	作者	年代	出处	被引量
	1	An improved scheme for minimum cross entropy threshold seleetion based on genetic algorithm 显示文摘	Tang Kezong Yuan Xiaojing Sun Tingkai	2011	Knowl-based Syst2011，24，8：	1
	2	Locality preserving CCA with application to data visualization and pose estimation显示文摘	Sun Tingkai Chen Songcan	2007	Im- age and Vision Computing2007，25，5：	1
	3	Localization with Incompletely Paired Data in Complex Wireless Sensor Network 显示文摘	Gu Jingjing Chen Songcan Sun Tingkai	2011	IEEE Transactions on WirelessCommunications2011，10，9：	1
	4	An improved scheme for minimum cross entropy threshold selection based on genetic algorithm显示文摘	Tang Kezong Yuan Xiaojing Sun Tingkai	2011	Knowledge-Based Systems2011，24，8：	1
	5	An improved scheme for minimum cross entropythreshold selection based on genetic algorithm显示文摘	TANG Kezong YUAN Xiaojing SUN Tingkai etal	2011	Knowledge-based Systems2011，24，8：	1
	6	An Improved Scheme for Minimum Cross Entropy Threshold Selection Based on Genetic Algorithm显示文摘	Tang Kezong Yuan Xiaojing Sun Tingkai	2011	Knowledge-based Systems2011，，9：	1
	7	An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems显示文摘	Tang Kezong Sun Tingkai Yang Jingyu	2011	Computers and Chemical Engineering2011，35，4：	1
	8	A self-adaptive linear evolutionary algorithm for solving constrained optimization problems显示文摘In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual's relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify 'good' individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.	Kezong TANG Jingyu YANG Shang GAO Tingkai SUN	2010	控制理论与应用(英文版)2010，8，4：	1
	9	Class-information-incorporated principal component analysis显示文摘	Chen Songcan Sun Tingkai	2005	Neurocomputing2005，69，13：	1
	10	MuhiKMHKS : a novel multiple kernel learning algorithm 显示文摘	Wang Zhe Chen Songcan Sun Tingkai	2008	IEEE Transcation Pattern Analysis Machine Intelligence2008，30，2：	1
	11	Class-information-incorporated principal component analysis 显示文摘	Chen Songcan Sun Tingkai	2005	Neurocomputing2005，69，：	1
	12	An improved scheme for minimum cross entropy thresh- old selection based on genetic algorithm显示文摘	Tang Kezong Sun Tingkai Yan Jingyu	2011	Knowl- edge-based Systems2011，3，：	1
	13	An improved genetic algorithm based on a novel selection strategy for nonlinearprogramming problems显示文摘	Tang Kezong Sun Tingkai Yang Jingyu	2011	Computers and Chemical Engineering2011，35，4：	1
	14	Loeatity preserving CCA with applications to data visualization and pose estimation 显示文摘	Sun Tingkai Chen Songcan	2007	Image and Vision Computing2007，25，5：	1
	15	Locality preserving CCA with applications to data visualization and pose estimation显示文摘	Tingkai Sun Chen S C	2007	Image and Vision Computing2007，25，5：	1

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