改进型分层抽样技术及性能研究

报文抽样技术是高速网络流量测量和管理中使用的一项关键技术。本文通过引进分层特征、层数L、分层边界、各层样本量分配、层内抽样策略5个分层抽样参数,并对其进行重新配置和简单理论探讨,实现对分层抽样技术的改进。同时文章使用简单线性估计推断原始流数据,并借助于Φ偏差检验方法,对改进的分层抽样技术和其它抽样技术在测量网络报文长度分布方面进行准确性性能比较。结果表明,改进的分层抽样技术在测量报文长度分布方面的准确性性能远高于其它抽样方式,提高了测量的精度。     

松散地层沉降变形FBG实时监测系统设计

为了及时发现第四系巨厚含水松散层沉降变形,提出了一种在预设钻孑L中埋入光纤Bragg光栅（FBG）传感器网络系统的监测方法。设计了松散层沉降变形的FBG实时监测系统,开发了一个储存监测数据的Microsoft Access数据库,采用MFCODBC方法访问和操作该数据库,并利用TeeChart控件动态显示实时监测曲线和历史监测曲线。该系统的监测实验表明：设备运行正常,监测系统稳定,实现了监测数据的高效处理和准确预测。     

Fast balanced sampling for highly stratified population

Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. A procedure to select balanced samples in a stratified population has previously been proposed. Unfortunately, this procedure becomes very slow as the number of strata increases and it even fails to select samples for some large numbers of strata. A new algorithm to select balanced samples in a stratified population is proposed. This new procedure is much faster than the existing one when the number of strata is large. Furthermore, this new procedure makes it possible to select samples for some large numbers of strata, which was impossible with the existing method. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. Finally, this algorithm turns out to be valuable for many applications as, for instance, for the handling of nonresponse.     

A data clustering algorithm for stratified data partitioning in artificial neural network

The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANNs). Researchers have proposed optimized data partitioning (ODP) and stratified data partitioning (SDP) methods to partition of input data into training, validation and test datasets. ODP methods based on genetic algorithm (GA) are computationally expensive as the random search space can be in the power of twenty or more for an average sized dataset. For SDP methods, clustering algorithms such as self organizing map (SOM) and fuzzy clustering (FC) are used to form strata. It is assumed that data points in any individual stratum are in close statistical agreement. Reported clustering algorithms are designed to form natural clusters. In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean. Further, these algorithms are computationally expensive as well. We propose a custom design clustering algorithm (CDCA) to overcome these shortcomings. Comparisons are made using three benchmark case studies, one each from classification, function approximation and prediction domains. The proposed CDCA data partitioning method is evaluated in comparison with SOM, FC and GA based data partitioning methods. It is found that the CDCA data partitioning method not only perform well but also reduces the average CPU time.     

防空火力分配建模及优化方法研究

在综合考虑防空对抗双方作战价值的基础上,建立了一种用于防空作战多武器系统对抗多批目标的火力分配模型,在此基础上,提出了基于粒子群优化（PSO）算法以及粒子群与遗传算法相结合（PSO—GA）的火力优化分配方法．通过仿真,并与遗传算法（GA）进行了比较,验证了火力分配模型的可行性以及所提出优化方法的有效性和优越性.     

A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.     

移动边缘计算中的卸载决策与资源分配策略

为在移动边缘计算服务器计算资源有限的情况下最小化系统总成本,提出一种多用户卸载决策与资源分配策略.优化任务执行位置选择和计算资源分配过程,对基于精英选择策略的遗传算法在编码、交叉、变异等操作方面进行改进,设计联合卸载决策与资源分配的improve-eGA算法.实验结果表明,与All_local、All_offload、...     

普通Kriging方法建立宏观地层的验证与评价

空间插值方法对空间变异性和空间相关性的反映程度及其准确性与精度直接影响到三维地质模型的真实性。普通Kriging方法应用于数字地层中,其变异函数模型和参数选择对结果影响很大,利用最值、方差、相关系数、平均误差以及误差分布统计为主要比较指标,采用交叉验证方法结合误差分析,对球状模型、指数模型、线性模型进行比较研究,初步得出线性有块金模型优于其他变异函数模型的结论,对数字地层的工程应用及其真实性评价有一定指导意义。     

A lexicographically fair allocation of discrete bandwidth for multirate multicast traffics

Fair bandwidth allocation is an important issue in the multicast network to serve each multicast traffic at a fair rate commensurate with the receiver's capabilities and the capacity of the path of the traffic. Lexicographically fair bandwidth layer allocation problem is considered and formulated as a nonlinear integer programming problem. A nonincreasing convex function of the bandwidth layers of the virtual sessions is employed to maximize the bandwidth of each virtual session from the smallest.To solve the fairness problem a genetic algorithm (GA) is developed based on the fitness function, ranking selection and the shift crossover. Outstanding performance is obtained by the proposed GA in various multicast networks. The effectiveness of the GA becomes more powerful as the network size increases.     

Spare parts allocation by improved genetic algorithm and Monte Carlo simulation

A combined Monte Carlo (MC) simulation and Genetic Algorithm (GA) method was proposed by other researchers for the optimisation of spare parts allocation. From case studies, it was found that the number of simulation trials of the existing method tended to be either excessive or inadequate. Thus, a simulation replication number control method making full use of the advance simulation effort is proposed and implemented into the existing method. A numerical example shows significant improvement on overall simulation efficiency and that at the same time the required accuracy is guaranteed. Furthermore, it is argued that application-specific knowledge should be embedded into the general GA procedure so that the evolution process can be more efficient. Heuristic methods for initial population preparation for GA with and without considering component cost difference are proposed and illustrated for spare parts allocation. A computing experiment was designed and performed to examine the influence of parameters for replication number control and initial population preparation. The generation of availability–cost curve further indicates the necessity to adopt heuristic methods to improve searching efficiency in GA.     

A global land-cover validation data set,part I: fundamental design principles

A number of land-cover products, both global and regional, have been produced and more are forthcoming. Assessing their accuracy would be greatly facilitated by a global validation database of reference sites that allows for comparative assessments of uncertainty for multiple land-cover data sets. We propose a stratified random sampling design for collecting reference data. Because the global validation database is intended to be applicable to a variety of land-cover products, the stratification should be implemented independently of any specific map to facilitate general utility of the data. The stratification implemented is based on the Köppen climate/vegetation classification and population density. A map of the Köppen classification was manually edited and intersected by two layers of population density and a land water mask. A total of 21 strata were defined and an initial global sample of 500 reference sites was selected, with each site being a 5?×?5 km block. The decision of how to allocate the sample size to strata was informed by examining the distribution of the sample area of land cover for two global products resulting from different sample size allocations to the 21 strata. The initial global sample of 500 sites selected from the Köppen-based stratification indicates that these strata can be used effectively to distribute sample sites among rarer land-cover classes of the two global maps examined, although the strata were not constructed using these maps. This is the first article of two, with the second paper presenting details of how the sampling design can be readily augmented to increase the sample size in targeted strata for the purpose of increasing the sample sizes for rare classes of a particular map being evaluated.     

Hybrid genetic algorithmsfor a multiple-objective scheduling problem

This paper describes the characteristics of two hybrid genetic algorithms (GAs) for generating allocation and sequencing of production lots in a flow-shop environment based on a non-linear, multi-criteria objective function. Both GAs are used as search techniques: in the first model the task of the GA is to allocate and sequence the jobs; in the second model, the GA is combined with a dispatching rule (Earliest Due Date, EDD) thus limiting its task only on the allocation of the jobs. Both GAs are characterized by a dynamic population size with dynamic birth rate, as well as by multiple-operator reproduction criteria and by adaptive crossover and mutation rates. A discrete-event simulation model has been used in order to evaluate the performances of the tentative schedules. The proposed algorithms have been subsequently compared with a classical branch and bound method.     

Allocating data to distributed-memory multiprocessors by genetic algorithms

We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. Experimental results show that the three GAs evolve good suboptimal solutions which are better than those produced by other methods. The GAs are also robust and do not show a bias towards particular problem configurations. The two parallel GAs have reasonable execution times, with the coarse-grain GA producing better solutions for the allocation of loosely synchronous computations.     

行为识别中基于GA优化的加速度特征选择方法

在基于加速度信号的人体行为识别中,LDA是较常用的特征降维方法之一,然而LDA并不直接以训练误差作为目标函数,无法保证获得训练误差最小的投影空间。针对这一情况,采用基于GA优化的LDA进行特征选择。提取加速度信号特征,利用PCA方法解决“小样本问题”,通过GA调整LDA中类间离散度矩阵的特征值矢量,使获得的投影空间训练误差最小。采用SVM对7种日常行为进行分类。实验结果表明,与单独采用PCA和采用PCA+LDA方法相比,基于GA优化的LDA算法在保证较高识别率的同时能有效降低特征维数并减小分类误差,最终测试样本的识别率可达95.96%。     

Methods of sampling based on exhaustive and evolutionary search

The development of mathematical software for training sampling is considered. Exhaustive and evolutionary sampling methods are developed. Criteria for selection, censoring, and pseudoclustering of instances are introduce in these methods. This makes it possible to speed up the sampling process and to ensure the compliance of the samples with the limited size. The proposed methods allow for the automatic allocation of a subset of instances with the minimal size from the original sample. The subset contains the most important instances for the model’s construction. The complexity estimates of the developed methods are defined. Experiments to determine the practical applicability of the methods are conducted. The use of the proposed estimates and identified dependences makes it possible to take into account the available computer resources during the sampling.     

Adaptive survey designs for sampling rare and clustered populations

Designing an efficient large-area survey is a challenge, especially in environmental science when many populations are rare and clustered. Adaptive and unequal probability sampling designs are appealing when populations are rare and clustered because survey effort can be targeted to subareas of high interest. For example, higher density subareas are usually of more interest than lower density areas. Adaptive and unequal probability sampling offer flexibility for designing a long-term survey because they can accommodate changes in survey objectives, changes in underlying environmental habitat, and changes in species-habitat models. There are many different adaptive sampling designs including adaptive cluster sampling, two-phase stratified sampling, two-stage sequential sampling, and complete allocation stratified sampling. Sample efficiency of these designs can be very high compared with simple random sampling. Large gains in efficiency can be made when survey effort is targeted to the subareas of the study site where there are clusters of individuals from the underlying population. These survey methods work by partitioning the study area in some way, into strata, or primary sample units, or in the case of adaptive cluster sampling, into networks. Survey effort is then adaptively allocated to the strata or primary unit where there is some indication of higher species counts. Having smaller, and more numerous, strata improves efficiency because it allows more effective targeting of the adaptive, second-phase survey effort.     

Structural topology and element sizing design optimisation of tall steel frameworks using a hybrid OC–GA method

This paper presents a hybrid optimisation method in which a local search operator based on a rigorously derived optimality criteria (OC) technique is embedded in the framework of a genetic algorithm (GA). The GA framework is particularly useful in the global exploration for optimal topologies, while the OC technique serves as a local search operator for efficient element sizing optimisation of given topologies. The hybrid OC–GA method was developed to strike a balance between the exploration of global search algorithms and the exploitation of efficient local search methods so as to make the hybrid method suitable for optimising tall building structures involving a large number of structural elements. The applicability and efficiency of the hybrid OC–GA method were tested with two 40-storey steel frameworks. The results show that the hybrid method can generate superior designs to pure GA while exhibiting rapid and smooth convergence, suggesting its great potential for optimising both structural form and element size of practical tall building structures.     

基于曲面建模的三维地质体体绘制研究

体绘制技术因为其相对于面绘制技术能够更好地反映三维体的真实结构,因而越来越受到人们的重视.提出了一种基于曲面建模的地质体体绘制方法,并在此基础上引入原始钻孔点的穷举搜索来获得散乱点以生成曲面.并根据现场数据的特征采用了适合中小型数据量插值拟合计算的改进谢别德插值来实现曲面的生成.产生曲面之后,使用了现在被普遍采用的基于图像空间序的光线投射算法完成地质体的体绘制,并在此基础上实现了剖切,缩放等三维体的交互操作.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

Entropy based region reducing genetic algorithm for reliability redundancy allocation in interval environment

This research paper presents a multi-objective reliability redundancy allocation problem for optimum system reliability and system cost with limitation on entropy of the system which is very essential for effective sustainability. Both crisp and interval-valued system parameters are considered for better realization of the model in more realistic sense. We propose that the system cost of the redundancy allocation problem depends on reliability of the components. A subpopulation and entropy based region reducing genetic algorithm (GA) with Laplace crossover and power mutation is proposed to determine the optimum number of redundant components at each stage of the system. The approach is demonstrated through the case study of a break lining manufacturing plant. A comprehensive study is conducted for comparing the performance of the proposed GA with the single-population based standard GA by evaluating the optimum system reliability and system cost with the optimum number of redundant components. Set of numerical examples are provided to illustrate the effectiveness of the redundancy allocation model based on the proposed optimization technique. We present a brief discussion on change of the system using graphical phenomenon due to the changes of parameters of the system. Comparative performance studies of the proposed GA with the standard GA demonstrate that the proposed GA is promising to solve the reliability redundancy optimization problem providing better optimum system reliability.