导弹武器射程评估的多因素正交试验设计方法

"问题驱动"式的教学理念、教学模式和方法是教学改革和发展的趋势.以导弹武器射程鉴定与评估为背景,探讨了正交试验设计的课程教学在其中的应用.通过对影响导弹武器射程因素的分析,在确定的水平条件下,进行正交试验设计,得到较好的组合方式.从理论分析和仿真案例,回答了多因素水平下的导弹射程试验评估问题.通过"问题驱动",不仅有利于提高学生学习兴趣和积极性,同时还有利于加深对理论和方法的认识和理解.     

基于基因表达谱的肿瘤识别与分类特征基因提取研究

针对基因表达谱信息基因提取的问题,使用Wilcoxon秩和检验方法进行"无关基因"的剔除,基于高低水平基因表达的特点,建立了关于高/低表达水平的双线性回归模型,基于残差分析提取了19个特征基因.使用启发式宽度优先搜索算法搜索最优基因子集,确定结肠癌的基因"标签",运用支持向量机对分类效果进行检验,分类效果良好.     

多种群协同进化策略下的虚拟企业基因重组

随着基因学说研究的深入,运用DNA理论来研究企业的可持续发展问题正逐渐成为管理学的热点.基于现有的企业DNA理论,本文首先从概念、结构、基因组要素识别及基因重组原理等方面对虚拟企业组织DNA的基本理论问题进行了系统研究.其次,在此基础上提出了采用多种群协同进化策略,通过生物学中的基因重组技术来实现虚拟企业可持续发展的思路.最后,研究了多种群协同进化策略对虚拟企业基因重组的影响,并建立了相应的基因重组模型.该研究不仅揭示出不同的进化策略,基因重组的效率效果不同;而且还充分论证了在多种群协同进化策略下,借助于基因重组技术,通过持续提高"基因"能力要素的竞争能力,能够有效保持虚拟企业在"市场生态"中的知识地位,从而实现可持续发展.     

基于不确定多属性群决策方法的装甲武器战力评估

针对方案的属性权重为实数、属性值为不确定语言,通过定义不确定语言的"依可能度不差于"关系和对影响装甲武器战斗潜力的几个属性进行分析,探讨了不确定语言之间"依可能度不差于"关系比较大小的充要条件,提出了一种基于该关系的多属性群决策方法,并给出了实例仿真.实例表明:方法进行评估步骤少,计算简单,容易施行.     

双特征参数基因识别算法实现

基因识别问题首要的工作是对数字化后的基因序列利用离散傅里叶变换(DFT)进行频谱分析.对于很长的DNA序列,功率谱或信噪比计算量很大,推导出了DNA序列在Voss映射、Z-curve映射和实数映射下的信噪比快速算法,以及在Voss映射与Z-curve映射下的信噪比的关系.针对阈值确定的问题提出了基于滑动窗口的局部阈值的算法,在分类时达到了很好的效果.另外,实现了基于移动序列信噪比曲线的基因识别方法.最后,由于DNA序列的3-周期性实际上反映了核苷酸在基因序列的三个子序列上分布的"非均衡性",因此引入"方差均值"特征来衡量该非均衡性,提出了基于方差均值的单因素基因识别方法及以信噪比和方差均值作为特征向量,并设计多项式分类器的基因识别算法.     

复杂疾病的分子网络模型研究

复杂疾病是危害人类健康的主要杀手.不同于单基因缺陷性遗传病,复杂疾病的发生发展与多个基因之间、基因与环境之间的相互作用有关,致病机理复杂,其早期诊断及治疗困难是21世纪生物医学研究的重大挑战之一.随着生物知识的不断积累和多层次"组学"数据的井喷式涌现,复杂疾病研究迎来了新的"组学革命",研究模式从以往的只关注某个分子扩展到对分子之间相互形成的生物分子网络的系统分析.作为系统生物学核心概念,生物分子网络系统整合大量生物知识和高通量生物数据,是研究复杂疾病的强有力工具.本文以分子网络为主线,以数学建模为工具来研究复杂疾病,针对复杂疾病关系和复杂疾病的发生发展机制等复杂疾病研究的关键热点问题,分析和集成高通量多层次组学数据,构建并求解生物分子网络的数学模型,在若干复杂疾病相关系统生物学问题中取得有生物学意义的结果.本文提出若干生物网络建模、分析及应用的方法并提供若干应用软件,为从系统层面理解复杂疾病提供重要参考;同时,网络模型在若干实例中的应用得到若干有生物学意义的结论,为揭示复杂疾病机理、推动疾病治疗与预防起到了一定的作用.     

一种新的武器总体综合设计方法

对武器装备的作战效能、寿命周期费用、风险和研制周期的模型进行了研究,提出了综合运用这四维指标作为目标函数的武器总体综合设计的思路和数学模型,可以全面而系统地认识武器装备的发展问题,避免设计中由于只注重提高性能而引起的弊端.     

大图图的飞船护甲

正虽然现在各个星球之间都很和睦,但是其实每个星球都在发展自己的武器,以防止在星际大战中落后。嘎啦王子经常到飞行器研究所去看科学家们发明的各种新奇飞船。"这些飞船可以以超快的速度在不同星球之间往返。"科学家大图图挺着肥胖的大肚子,自豪地介绍道。"为什么它们可以飞行那么久?"嘎啦王子一下子就问到了问题的关键。     

基于均匀先验分布武器射程评定的Bayes方法

应用Bayes统计方法进行武器射程的评定,可以有效减少试验的样本量,节省试验弹药,Bayes方法的关键是确定先验分布,均匀分布是比较容易确定的一种先验分布,该文给出了基于均匀先验分布武器射程评定的Bayes方法,对改进射程的评定方法,减少试验用弹量,具有重要作用.     

另辟蹊径 柳暗花明——逆向思维解决数学难题实例三则

逆向思维是指按常规思维方式思考问题受阻时,转换思维角度,从问题的对立面思考的思维方法,常用于解决直接证明难以奏效的问题,是解决数学难题的一件有力武器.实例一五次方程的挑战早在公元前2000多年,巴比伦人就已经用"楔形文字"在泥板上记载下了一元二次方程的求根公式;到了9世纪,数学家阿尔·花拉子模给出了一般的一元二次方程的解法.16世纪,意     

基于遗传算法的装备维修费用优化分配理论及优化方法研究

在美国工业界武器系统咨询委员会提出的郊能公式基础上 ,建立了武器装备效能与维修费用函数关系 ,为武器装备维修费用的优化分配提供了一个标准 .针对多种武器装备维修费用优化 ,建立了一个维修费用分配模型 ,用遗传算法进行了优化 ,并对优化过程进行了详细阐述 .在此基础上开发了装备维修经费优化与管理辅助决策支持系统 .     

Identifying indicators of chemical,biological, radiological,and nuclear (CBRN) weapons development activity in sub-national terrorist groups

Data collected on known terrorist organizations allow intelligence agencies to build a statistical database of features for each group and an observed level of development of chemical, biological, radiological, or nuclear (CBRN) weapons. For the intelligence analyst, a statistical exploration of the structure of the multivariate data is helpful for determining which subset of features—and the relative contribution of each feature in the subset—best discriminate between levels of CBRN weapons development. The resulting function that is used to discriminate between CBRN development levels is called the ‘classifier’. Once the appropriate subset of indicators has been identified and a classifier developed, intelligence agencies will be better able to focus their information gathering and to assess the effect that changes in a terrorist group's features will have on their CBRN weapons development. Additionally, the classifier will enable the intelligence agency to predict the CBRN weapons development level of terrorist group where the feature set of the group is known but the level is unknown. In this analysis, we compare three approaches for building a classifier that best predicts CBRN weapons development levels using a training set with 45 observations; (1) heuristic pattern recognition approach that couples a weighted Minkowski distance metric with a nonparametric kernel-based classification method, (2) classification trees, and (3) discriminant analysis. Where possible, cross-validation is conducted on the data to ensure that the resulting classifier is not overly dependent on the training set. This initial analysis provides some interesting results and suggests a reasonable starting point for finding structure in the data as more observations are added.     

Genetic algorithms: Foundations and applications

Genetic algorithms are defined. Attention is directed to why they work: schemas and building blocks, implicit parallelism, and exponentially biased sampling of the better schema. Why they fail and how undesirable behavior can be overcome is discussed. Current genetic algorithm practice is summarized. Five successful applications are illustrated: image registration, AEGIS surveillance, network configuration, prisoner's dilemma, and gas pipeline control. Three classes of problems for which genetic algorithms are ill suited are illustrated: ordering problems, smooth optimization problems, and totally indecomposable problems.     

BP-GA混合优化策略在人力资源战略规划中的应用

采用混合优化策略训练神经网络,进而实现地区人力资源数据的时间序列预测.神经网络,尤其是应用反向传播(back propagation,简称BP)算法训练的神经网络,被广泛应用于预测中.但是BP神经网络训练速度慢、容易陷入局部极值.遗传算法(genetic algorithm,简称GA)具有很好的全局寻优性.因而提出将BP和GA结合起来的混合优化策略训练神经网络,来实现人力资源数据预测.与BP算法相比,数值计算结果表明预测精度高、速度快,为地区人力资源数据的时间序列预测研究提供了一条新的途径.     

A Cellular Genetic Algorithm with Disturbances: Optimisation Using Dynamic Spatial Interactions

This paper describes a novel evolutionary algorithm inspired by the nature of spatial interactions in ecological systems. The Cellular Genetic Algorithm with Disturbances (CGAD) can be seen as a hybrid between a fine-grained and a coarse-grained parallel genetic algorithm. The introduction of a disturbance-colonisation cycle provides a mechanism for maintaining flexible subpopulation sizes and self-adaptive controls on migration. Experiments conducted, using a range of stationary and non-stationary optimisation problems, show how changes in the structure of the environment can lead to changes in selective pressure, population diversity and subsequently solution quality. The significance of the disturbance events lies in the new ecological patterns that arise during the recovery phase.     

Finding small somatic structural variants in exome sequencing data: a machine learning approach

Genetic variation forms the basis for diversity but can as well be harmful and cause diseases, such as tumors. Structural variants (SV) are an example of complex genetic variations that comprise of many nucleotides ranging up to several megabases. Based on recent developments in sequencing technology it has become feasable to elucidate the genetic state of a person’s genes (i.e. the exome) or even the complete genome. Here, a machine learning approach is presented to find small disease-related SVs with the help of sequencing data. The method uses differences in characteristics of mapping patterns between tumor and normal samples at a genomic locus. This way, the method aims to be directly applicable for exome sequencing data to improve detection of SVs since specific SV detection methods are currently lacking. The method has been evaluated based on a simulation study as well as with exome data of patients with acute myeloid leukemia. An implementation of the algorithm is available at https://github.com/lenz99-/svmod.     

A dynamic genetic algorithm based on continuous neural networks for a kind of non-convex optimization problems

This paper presents a kind of dynamic genetic algorithm based on a continuous neural network, which is intrinsically the steepest decent method for constrained optimization problems. The proposed algorithm combines the local searching ability of the steepest decent methods with the global searching ability of genetic algorithms. Genetic algorithms are used to decide each initial point of the steepest decent methods so that all the initial points can be searched intelligently. The steepest decent methods are employed to decide the fitness of genetic algorithms so that some good initial points can be selected. The proposed algorithm is motivated theoretically and biologically. It can be used to solve a non-convex optimization problem which is quadratic and even more non-linear. Compared with standard genetic algorithms, it can improve the precision of the solution while decreasing the searching scale. In contrast to the ordinary steepest decent method, it can obtain global sub-optimal solution while lessening the complexity of calculation.     

Automated code generation by local search

There are many successful evolutionary computation techniques for automatic program generation, with the best known, perhaps, being genetic programming. Genetic programming has obtained human competitive results, even infringing on patented inventions. The majority of the scientific literature on automatic program generation employs such population-based search approaches, to allow a computer system to search a space of programs. In this paper, we present an alternative approach based on local search. There are many local search methodologies that allow successful search of a solution space, based on maintaining a single incumbent solution and searching its neighbourhood. However, use of these methodologies in searching a space of programs has not yet been systematically investigated. The contribution of this paper is to show that a local search of programs can be more successful at automatic program generation than current nature inspired evolutionary computation methodologies.     

OMEGA one multi ethnic genetic approach

The genetic algorithm (GA) is a quite efficient paradigm to solve several optimization problems. It is substantially a search technique that uses an ever-changing neighborhood structure related to a population which evolves according to a number of genetic operators. In the GA framework many techniques have been devised to escape from a local optimum when the algorithm fails in locating the global one. To this aim we present a variant of the GA which we call OMEGA (One Multi Ethnic Genetic Approach). The main difference is that, starting from an initial population, \(k\) different sub-populations are produced at each iteration and they independently evolve in \(k\) different environments. The resulting sub–populations are then recombined and the process is iterated. Our basic algorithmic scheme is tested on a recent and well-studied variant of the classic problem of the minimum spanning tree: the Minimum Labeling Spanning Tree problem. We compare our algorithm with several approaches drawn from the literature. The results are encouraging in view of future application of OMEGA to other classes of problems.     

Test driving three 1995 genetic algorithms: New test functions and geometric matching

Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several different types of genetic algorithms with varying performance and search characteristics. In this article we look at three genetic algorithms: an elitist simple genetic algorithm, the CHC algorithm and Genitor. One problem in comparing algorithms is that most test problems in the genetic algorithm literature can be solved using simple local search methods. In this article, the three algorithms are compared using new test problems that are not readily solved using simple local search methods. We then compare a local search method to genetic algorithms for geometric matching and examine a hybrid algorithm that combines local and genetic search. The geometric matching problem matches a model (e.g., a line drawing) to a subset of lines contained in a field of line fragments. Local search is currently the best known method for solving general geometric matching problems.