Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Evolving BlenX programs to simulate the evolution of biological networks

We present a formal approach to study the evolution of biological networks. We use the Beta Workbench and its BlenX language to model and simulate networks in connection with evolutionary algorithms. Mutations are done on the structure of BlenX programs and networks are selected at any generation by using a fitness function. The feasibility of the approach is illustrated with a simple example.     

Expected runtimes of evolutionary algorithms for the Eulerian cycle problem

Evolutionary algorithms are randomized search heuristics, which are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. They have successfully been applied to different kinds of arc routing problems. To start the analysis of evolutionary algorithms with respect to the expected optimization time on these problems, we consider the Eulerian cycle problem. We show that a variant of the well-known (1+1)$(1+1)$ EA working on the important encoding of permutations is able to find an Eulerian tour of an Eulerian graph in expected polynomial time. Altering the operator used for mutation in the considered algorithm, the expected optimization time changes from polynomial to exponential.     

Parallel Lagrange interpolation on <Emphasis Type="Italic">k</Emphasis>-ary <Emphasis Type="Italic">n</Emphasis>-cubes with maximum channel utilization

This paper proposes an efficient parallel algorithm for computing Lagrange interpolation on k-ary n-cube networks. This is done using the fact that a k-ary n-cube can be decomposed into n link-disjoint Hamiltonian cycles. Using these n link-disjoint cycles, we interpolate Lagrange polynomial using full bandwidth of the employed network. Communication in the main phase of the algorithm is based on an all-to-all broadcast algorithm on the n link-disjoint Hamiltonian cycles exploiting all network channels, and thus, resulting in high-efficiency in using network resources. A performance evaluation of the proposed algorithm reveals an optimum speedup for a typical range of system parameters used in current state-of-the-art implementations.

基于遗传算法和广义交叉原理求解正则参数

本文研究了正则化方法中正则参数的求解问题,提出了一种新的正则参数求解策略,即利用遗传算法基于广义交叉检验准则求解正则参数,数值模拟验证了该方法的可行性和有效性。     

基于模板的SoC结构自动划分方法

针对SoC芯片设计中的系统划分问题,提出一种面向应用的系统结构自动划分方法--基于模板的系统划分方法,并开发了系统划分工具--TBPS.基于模板的系统划分方法通过提取应用描述中的相似结构,并使用相似结构划分系统,最终得到一个尽量复用的系统结构.系统的相似结构划分采用遗传算法实现最优覆盖的搜索,并使用贪婪算法实现资源配置和任务调度.与已有的在系统划分中事先设定目标结构方法不同,文中方法可以根据应用的特点自动生成划分的结构.采用该方法及TBPS实现了应用描述到系统结构的自动划分.实验结果表明了TBPS对数据处理为主的应用划分的有效性.     

一种变论域模糊控制自适应算法

针对受控系统非线性、时变性、复杂性和不确定性的特点, 文中采用一种基于模糊控制的变论域自适应控制算法, 将变论域引入模糊控制的隶属函数中, 设计了输入函数的自适应律, 并通过李雅普诺夫函数进行了稳定性分析. 仿真结果表明在有干扰的情况下, 算法能很好的跟踪系统输入, 使系统跟踪误差小且能够保证系统的稳定性.该算法结构简单, 具有良好的鲁棒性和动态性能, 同时克服了系统参数变化对稳定性造成的不利影响. 仿真实例表明了本算法的正确性和有效性.     

一种特征加权FCM算法的图像重建技术研究

针对传统FCM(Fuzzy C-means)算法中初始聚类中心选取的随机性以及对初始值敏感的问题,提出一种基于进化策略的色彩空间加权的FCM聚类算法.通过在RGB(Red Green Blue)色彩空间矢量中设置加权矩阵来补偿各色彩的非均匀性,并采用一种类内最小距离最大的统计聚类算法来初始化聚类中心.实验结果表明,该算法能有效减少颜色量化后的均方差值,保持重建图像的整体层次和局部特征细节,对研究图像处理技术有较强的实际意义.

Abstract：

Aiming at a defect on randomness of the initial clustering center choosing and sensitivity of initial value in tradition FCM(fuzzy C-means) algorithm, a clustering algorithm about FCM of weighted color space based on evolutionary strategy is proposed. By interposing weighted matrix in RGB(Red Green Blue) color space, the color's inhomogeneous is compensated. And by using a statistics clustering algorithm of minimal maximal distance, clustering center is initiated. The experimental results show that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction, and has practical value to the study of the image process technology.     

贝叶斯网参数学习中连续变量离散化方法研究

连续变量离散化是贝叶斯网络参数学习中面临的一个重要问题,它的好坏将直接影响到贝叶斯网络的推理效果。目前缺少一种有效的手段用于评价连续变量离散化的好坏,通过研究,提出了推理信息量的概念,并采用作为衡量连续变量离散化好坏的标准。在连续变量离散化的过程中,采用遗传算法通过迭代的方式寻求最优解,其中,推理信息量作为衡量个体适应度的标准。实例分析证明,推理信息量大的推理效果好要优于推理信息量小的推理效果。     

基于非结构化控制流的进化测试方法

结构性测试中,进化测试是一种高效的自动生成高质量测试用例的技术.然而,当程序中出现非结构化特征时,在面向节点的测试标准下,其效率极其低下甚至不及随机测试生成方法.在考虑循环体内部出现跳转(goto、retum)语句的情况下,提出了一种适应度计算方法.该方法在结合传统进化测试适应度计算的基础上,充分考虑了循环次数对于进化搜索的影响.实验结果表明,本适应度函数可以很好地引导进化搜索,并以较小的代价生成测试用例.