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 The prediction of RNA secondary structure is used in the functional analysis of the protein. It has important significance in bioinformatics. The problem for predicting RNA secondary structure containing pseudoknots is NPcomplete.It is one of the two basic methods to predict RNA secondary structure by themodynamics minimal free energy method. With the number of new published sequences increasing by index, this method is applied more and more, and it has become the first method selected to predict RNA secondary... The prediction of RNA secondary structure is used in the functional analysis of the protein. It has important significance in bioinformatics. The problem for predicting RNA secondary structure containing pseudoknots is NPcomplete.It is one of the two basic methods to predict RNA secondary structure by themodynamics minimal free energy method. With the number of new published sequences increasing by index, this method is applied more and more, and it has become the first method selected to predict RNA secondary structure. Mfold algorithm used widely can only compute nested RNA secondary structure that pseudoknots aren't permitted in it. Most methods for RNA that are capable of folding pseudoknots adopt heuristic search procedures, but their results are not surely of optimal value. Rivas algorithm and Lyngsф algorithm are the best as we know to predict RNA secondary structure including pseudoknots.Lyngsф algorithm can only compute one planar pseudoknot, and its time complexity is O (n~5) and its space complexity is O (n~3). In this paper, an algorithm based on Greedy with time complexity O(n~2) is presented to predict the secondary structure of RNA sequence. Based on the principle that the stacking stems in RNA molcules are relatively more stable, the algorithm targets to compute the secondary structure with maximum stackings. We implement the algorithm in VC++ and use the RNA subsequence in the PseudoBase to computational experiment. The experiment results indicate that the algorithm has good accuracy of prediction. The algorithm can predict nested secondary structures and pseudoknoted secondary structures of RNA predict molecules.  RNA二级结构预测用于蛋白质功能分析,在生物信息学研究中具有重要意义.提出了一个时间复杂度为O(n2)的基于Greedy算法思想的算法.基于“堆迭结构相对稳定”的RNA分子结构特征,算法思想为计算具有最多堆迭的RNA二级结构.用VC++编程实现了该算法,采用PseudoBase的RNA分子片段进行了计算实验,结果表明该算法具有良好的准确度.该算法可预测RNA分子的嵌套二级结构和伪结点二级结构.  The optimization problem of uncertain structures based on nonprobabilistic reliability is investigated.The uncertain parameters are bounded by convex models.The nonprobabilistic reliability index is adopted to evaluate safety of structures and taken as a design constraint so that the optimum design can resist a given level of uncertainty.Since the nonprobabilistic reliability index is defined by a minmax model,the reliabilitybased design is a twolevel optimization problem that involves a nested loop procedure... The optimization problem of uncertain structures based on nonprobabilistic reliability is investigated.The uncertain parameters are bounded by convex models.The nonprobabilistic reliability index is adopted to evaluate safety of structures and taken as a design constraint so that the optimum design can resist a given level of uncertainty.Since the nonprobabilistic reliability index is defined by a minmax model,the reliabilitybased design is a twolevel optimization problem that involves a nested loop procedure for the overall optimization and iterative reliability evaluation.To solve this problem efficiently,a sequential linearization method is developed.The method is easily integrated into sequential linear programming(SLP) algorithm and can be used in cases of the mixture of ellipsoidal convex models and interval variables.A simple example is given to demonstrate the proposed approach.  基于不确定参量的凸集合描述,研究了考虑非概率可靠性约束时,结构优化设计模型的求解问题。由于非概率可靠性指标是用一个极小极大模型来定义的,故以该指标作为设计约束,将得到一个嵌套的二级优化模型。为了求解该模型,提出了一种序列线性化的计算方法。利用非概率可靠性分析的拉格朗日乘子,逐步构造可靠性指标的一阶近似,通过序列线性规划法求解二级优化问题。该算法可用于区间变量和超椭球凸集模型并存的情形,具有较好的适用性。论文给出了主要的敏度计算公式,并通过简单算例对所提算法进行了验证。  A new hybrid evolution algorithm named MGEPGA was proposed.It is based on the new concept of the multilayer chromosomes in gene expression programming (GEP) and uses twolevel evolution method,whose main idea is to embed Genetic Algorithm (GA) in GEP where GEP is employed to optimize the structure of a model,and GA is employed to optimize the parameters of a model.The results of experiments show that the algorithm has better search ability and higher stability than the traditional single gene and multigenes... A new hybrid evolution algorithm named MGEPGA was proposed.It is based on the new concept of the multilayer chromosomes in gene expression programming (GEP) and uses twolevel evolution method,whose main idea is to embed Genetic Algorithm (GA) in GEP where GEP is employed to optimize the structure of a model,and GA is employed to optimize the parameters of a model.The results of experiments show that the algorithm has better search ability and higher stability than the traditional single gene and multigenes GEP and GPGA method.  提出了一种新的基于多层染色体基因表达式程序设计的混合遗传进化算法:MGEPGA。该算法在基因表达式程序设计的基础上引入了多层染色体,并采用与遗传算法相嵌套的二级演化方法。利用染色体构建的层次调用模型对个体进行表达,用基因表达式程序设计方法优化模型结构,遗传算法优化模型参数。通过对三组数据测试,与用单基因GEP、多基因GEP的结果进行对比,实验表明改进的算法具有更强的寻优能力和更高的稳定性。  
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