Feature Selection Based on Difference and Similitude in Data Mining

Feature selection is the pretreatment of data mining. Heuristic search algorithms are often used for this subject. Many heuristic search algorithms are based on discernibility matrices, which only consider the difference in information system. Because the similar characteristics are not revealed in discernibility matrix, the result may not be the simplest rules. Although differencesimilitude（DS） methods take both of the difference and the similitude into account, the existing search strategy will cause some important features to be ignored. An improved DS based algorithm is proposed to solve this problem in this paper. An attribute rank function, which considers both of the difference and similitude in feature selection, is defined in the improved algorithm. Experiments show that it is an effective algorithm, especially for large-scale databases. The time complexity of the algorithm is O（｜ C ｜^2｜U ｜^2）.     

货郎问题求解算法分析

介绍了求解货郎问题的4个算法：贪心算法、MST近似算法、MM近似算法和回溯搜索算法。分别使用各个算法对一个货郎问题的具体实例进行求解，并对各个算法的性能进行了分析比较。贪心算法的运行速度较快，但在大多数情况下该算法找到的是次优解而非最优解。MST和MM近似算法用以求解满足三角不等式的货郎问题，其近似性能比（即精确度）分别为：RMST（I）＜2，RMM（I）＜3／2。回溯搜索算法可以求出货郎问题的最优解，随着城市数目的增加，其搜索效率会下降。     

A niche-based evolutionary Tabu search algorithm for optimal coordination of protection relays in power networks

In this paper, refusal of operation and the degree of constraint violation are considered as one part of the objective function for optimizing the protection relay setting coordination of over current relays in power systems; a new expression of objective function in this optimization problem is proposed. To combine the advantages of both Tabu search (TS) and evolutionary algorithms (EAs), a new niche-based evolutionary Tabu search algorithm (named NETS) is presented to solve this optimization problem. Moreover, in two cases of power networks, comparisons between NETS and two published algorithms are given. Experimental results show the expression of the objective function for protection relay setting is feasible and reasonable, and the proposed algorithm NETS exhibits a good performance. Biography: YUAN Rongxiang (1965–), male, Professor, Ph. D., research direction: safety and stability in distributed power networks, relay protection, information and intelligent of power system.     

Solving a kind of high complexity multi-objective problems by a fast algorithm

A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions. Foundation item: Supported by the National Natural Science Foundation of China (60204001, 70071042, 60073043, 60133010) and Youth Chengguang Project of Science and Technology of Wuhan City (20025001002). Biography: Zeng San-you ( 1963-), male, Associate professor, research direction: evolutionary computing, parallel computing     

Stable initialization scheme for K -means clustering

Though K-means is very popular for general clustering, its performance which generally converges to numerous local minima depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor (RNN) search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of proposed algorithm to K-means clustering algorithm is demonstrated. Experiment is carried out on several popular datasets and the results show the advantages of the proposed method. Foundation item: Supported by the National Natural Science Foundation of China (60503020, 60503033, 60703086), the Natural Science Foundation of Jiangsu Province (BK2006094), the Opening Foundation of Jiangsu Key Laboratory of Computer Information Processing Technology in Soochow University (KJS0714) and the Research Foundation of Nanjing University of Posts and Telecommunications (NY207052, NY207082)     

一种新的快速块匹配运动估计算法

提出了一种新的快速、有效的块匹配运动估计算法。算法采用多步搜索方法,利用相邻块之间的运动相关性,选择反映当前块运动趋势的预测点作为初始搜索点;用子采样块匹配失真度量来减少计算量;利用运动矢量的中心倾向的分布特性,用多侯选点钻石形状搜索方法来提高运动估计的速度和准确性。实验表明,该算法计算复杂性低,预测质量好。     

微粒群优化在Job-shop调度中的应用

Job-shop调度问题是典型的NP-难问题,利用微粒群优化的全局搜索能力和高搜索效率以及模拟退火算法的局部搜索能力,发展了一种快速、且易于实现的新的混合启发式算法,并将其应用于求解标准Job-shop调度问题,计算结果以及与其他算法的比较说明,该算法是一种求解Job-shop调度问题的可行且高效的方法。     

A gene-pool based genetic algorithm for TSP

Based on the analysis of previous genetic algorithms (GAs) for TSP, a novel method called Ge- GA is proposed. It combines gene pool and GA so as to direct the evolution of the whole population. The core of Ge- GA is the construction of gene pool and how to apply it to GA. Different from standard GAs, Ge- GA aims to enhance the ability of exploration and exploitation by incorporating global search with local search. On one hand a local search called Ge- Lo-calSearch operator is proposed to improve the solution quality, on the other hand the modified Inver-Over operator called Ge- InverOver is considered as a global search mechanism to expand solution space of local minimal. Both of these operators are based on the gene pool. Our algorithm is applied to 11 well-known traveling salesman problems whose numbers of cities are from 70 to 1577 cities. The experiments results indicate that Ge- GA has great robustness for TSP. For each test instance, the average value of solution quality, found in accepted time, stays within 0. 001% from the optimum. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043, and 60133010) Biography: Yang Hui ( 1979-), female, Master candidate, research direction; evolutionary computation.     

Incremental Web Usage Mining Based on Active Ant Colony Clustering

To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and ＂flying over the plane＂ moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users＇ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.     

一种用于RBF神经网络的支持向量机与BP的混合学习算法

基于支持向量机与径向基(RBF)神经网络在结构上的相似性,提出了一种用于RBF网络的支持向量机与BP的混合学习算法．算法分为2步：首先采用序贯最小优化算法学习训练支持向量机,得到RBF网络较优的初始结构和参数;随后由BP算法调整优化RBF网络参数．混合学习算法结合了支持向量机小样本学习、学习训练快捷以及BP算法在线修改网络参数的特点．仿真研究表明,混合学习算法学习效率高,网络性能优良,应用于函数逼近时效果优良．     

应用基因概率学习算法求解最小码覆盖问题

概述最小码覆盖问题，以及现有的几种求解最小码覆盖问题的计算机搜索算法．在基因概率学习算法(PBIL)的基础上，建立码覆盖问题的目标函数，引进启发式算子HF0，针对局部陷阱设计跳出策略，从而获得一种新的快速求解码覆盖问题的算法．     

ISTC： A New Method for Clustering Search Results

A new common phrase scoring method is proposed according to term frequency-inverse document frequency （TFIDF） and independence of the phrase. Combining the two properties can help identify more reasonable common phrases, which improve the accuracy of clustering. Also, the equation to measure the in-dependence of a phrase is proposed in this paper. The new algorithm which improves suffix tree clustering algorithm （STC） is named as improved suffix tree clustering （ISTC）. To validate the proposed algorithm, a prototype system is implemented and used to cluster several groups of web search results obtained from Google search engine. Experimental results show that the improved algorithm offers higher accuracy than traditional suffix tree clustering.     

基于改进布谷鸟算法结合电导增量法的最大功率点追踪

动态遮阴下,光伏阵列的输出P-U曲线会出现多个功率极值点,传统最大功率点追踪会陷入局部最优。为此,本文提出基于自适应差分进化的改进布谷鸟(ICS)算法与电导增量法(INC)相结合的复合算法(ICS-INC)。该算法提出自适应抛弃概率和自适应步长因子,结合差分变异进行随机偏好游走,使算法的搜索开发能力得到提升,避免陷入局部最优。通过改进Lévy飞行公式减小其随机性,减小算法的迭代次数来缩短跟踪时间,由INC实现局部快速搜索,稳定输出最大功率。仿真结果表明,与其他算法相比,该算法的追踪速度,全局搜索性以及环境变化的适应能力均得到提升。     

无源互调干扰的二维时延自适应估计算法

针对影响通信接收机性能的时变性无源互调干扰问题,采用幂级数模型对无源互调干扰进行建模,从工程的角度给出了基于二分法、快速二分法和并行法的3种二维时延自适应估计算法,并对其性能进行了对比.仿真表明3种搜索算法经过不同次数的迭代后,均可估得二维时延值.其中,基于快速二分法的估计算法能以相对较低的资源消耗量获得较快的跟踪速度,更适合硬件实现.实测表明,原理样机采用基于快速二分法的二维时延估计算法,可对PIM信号实现更好的估计效果,补偿后的信号经自适应滤波可获得良好的性能.      

A smart mathematic morphology method for recognition and understanting of airfield

A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can be divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance. Foundation item: Supported by the National Defence Foundation of China (51401040603) Biography: WANG Zhi-cheng (1975-), male, Ph. D. candidate, research direction: medical images analysis, ATR.     

A two-level subspace evolutionary algorithm for solving multi-modal function optimization problems

In this paper, a new algorithm for solving multimodal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043, 60133010). Biography: Li Yan( 1974-), female, Ph. D candidate, research interest: evolutionary computation.     

遗传算法在PCB板元件检测中的应用

介绍了一种基于图像模板匹配算法的PCB板元件自动快速检测方法．从实际应用出发，找到了一种合适的图像相似性度量方法．提出利用代间差分遗传算法优化其搜索匹配速度，给出了算法实现的全过程．用实际拍摄的PCB板元件进行了性能测试，验证了代间差分遗传算法能提高检测速度．     

基于空间相关预测的快速块匹配运动估计算法

为了提高自适应十字搜索(adaptiveroodpatternsearch,ARPS)算法中运动估计的速度和准确性,提出一种基于空间相关预测的快速块匹配运动估计算法.根据块匹配度量准则,将邻域块中与当前块相似度最高和次高的两个运动向量(motionvector,MV)的均值作为当前块的预测MV,改进了传统ARPS算法的固定单块预测模式,增强了起始搜索中心位置的预测,减小了由于运动变化而引起的预测误差.实验结果表明所提算法与ARPS和其他标准快速块匹配运动估计方法相比,有效地减少了计算复杂度,提高了配准精度.     

一种新型快速的直接随机优化算法

 针对常用优化算法求解时实时性较差且易陷于局部最优解的问题, 提出一种新型快速的直接随机优化算法(DROA). 该算法直接利用随机搜索过程寻找最优解, 减少了额外计算, 降低了计算复杂度; 其搜索过程分为全局搜索和局部搜索两个阶段, 各阶段选用不同的调节参数公式和搜索方式. 先将递增参数的3个随机优化模块串接构造全局优化子, 并将多个全局优化子并行搜索构造全局优化器以获得全局最优解; 再将多个局部优化模块串接在一起运行构造局部优化器使优化解更精确. 测试结果表明, 该方法快速高效, 优于目前的全局优化算法.     

改进的VBLAST检测算法及其在多载波CDMA系统中的应用

将多输入多输出(MIMO)技术应用于传统的MC\DS CDMA方案,构成MIMO MC\DS CDMA系统,能在很大程度上提高MC\DS CDMA方案的性能。在深入研究低复杂度的QR分解相关算法的基础上,提出了一种MIMOMC\DS CDMA系统中改进的VBLAST检测算法——平行干扰消除QR分解检测算法,该算法进一步抑制了误码传播现象。仿真结果表明,所提出的算法无论是在单用户或者多用户条件下都能显著提高系统的误码率性能。