兆程序设计与软件过程驱动的软件开发

兆程序设计与软件过程驱动的软件开发是新一代的软件工程技术，本文首先介绍了这项技术的背景；软件工程对软件本质的研究；软件环境技术，重用技术的成熟。８０年代网络技术，面向对象，多媒体，原型开发技术的发展，导致了集成技术对集成框架的研究。     

TURBO PASCAL程序设计中几个难题的最新解决方法

程序代码长度或参加运算的数据量超过内存限制,可调数组的实现,以及过程和函数作为参数传递:这三个难题时常困扰着用TURBO PASCAL的程序设计者、经过对TURBO PASCAL的分析研究,和在软件设计中的反复探索,本文提出用覆盖、强制类型转换和过程类型作为解决上述难题的最新方法。从而使得小内存运行大程序和设计通用程序都变得轻而易举。     

程序分层结构验证方法

分层结构对使用PASCAL、ALGOL、FORTRAN、COBOL等语言的用户来说,是一种常用的程序结构。本文引入一个囊括过程调用验证规则的公理系统,以适应日益受到广泛注意的分层结构验证技术的需要。文中提出了“调用不变式”概念并用于过程验证规则中,使验证过程不需对过程参数作详细划分,使验证更为方便、实用。     

面向对象的程序设计模拟细胞有丝分裂

介绍了在微机上编程模拟生长及周期分裂过程,提出了一个面向对象的细胞类细胞,为利用计算机辅助研究细胞分裂作了一次新的探讨。     

“傻瓜”数据录入窗口设计原理及程序

数据库编程人员都希望系统能根据用户提供的字段名与汉字名对应表,自动生成一个漂亮的数据录入窗口,但目前所有的数据库都没有这样的功能。笔者经过对录入窗口生成过程的认真研究。找到了解决这一问题的方法。并用FoxBASE2.1编制了一个实现这一功能的过程文件。 设计原理:数据录入窗口设计主要参数有:汉字名称、字段名、数据录入格式及各位置坐标。汉字名     

程序控制流图：一种可视化的程序设计工具

本文详细地介绍了一种可视化的程序设计工具：控制流图。该图表示一个过程内所有基本块执行的可能流向。也能反映一个过程的实时执行过程，除了描述ＣＦＧ所具有的功能外，本文还论述了ＣＦＧ的实现途径以及使用方法，最后，给出了进一步改进的方向。     

Delphi中多线程技术的应用

研究Delphi中线程使用过程中应当注意的VCL线程的安全性与线程使用过程中的同步的有关问题。     

通过端口寄存器编程捕捉屏幕图形

介绍了捕捉专用软件GBCUV屏幕图形的驻留程序设计原理及过程。研究发现,当某些软件设置有防屏幕拷贝措施时,可以通过图形端口寄存器的编程,读写图形缓冲区指定页的内容。     

程序设计类课程思政案例库的建设与实践

分析程序设计类课程思政教育研究现状,提出思政案例库的构建方法并以实例说明思政案例库设计原则、设计要素,介绍思政元素与教学知识点、教学过程的有机融合过程,最后说明思政教育实施效果。     

程序设计发展综述

从程序设计语言、程序设计方法、程序的集成开发环境等三方面来论述程序设计的发展过程,并指出了未来程序设计发展的三个方向。     

Evolutionary Techniques for Web Caching

Web caching has been proposed as an effective solution to the problems of network traffic and congestion, Web objects access and Web load balancing. This paper presents a model for optimizing Web cache content by applying either a genetic algorithm or an evolutionary programming scheme for Web cache content replacement. Three policies are proposed for each of the genetic algorithm and the evolutionary programming techniques, in relation to objects staleness factors and retrieval rates. A simulation model is developed and long term trace-driven simulation is used to experiment on the proposed techniques. The results indicate that all evolutionary techniques are beneficial to the cache replacement, compared to the conventional replacement applied in most Web cache server. Under an appropriate objective function the genetic algorithm has been proven to be the best of all approaches with respect to cache hit and byte hit ratios.     

Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.     

Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm

With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques. Besides improved computation efficiency, parallelization may bring about innovation to many aspects of evolutionary algorithms. In this article, we focus on the effect of variable population size on accelerating evolution in the context of a parallel evolutionary algorithm. In nature it is observed that dramatic variations of population size have considerable impact on evolution. Interestingly, the property of variable population size here arises implicitly and naturally from the algorithm rather than through intentional design. To investigate the effect of variable population size in such a parallel algorithm, evolution dynamics, including fitness progression and population diversity variation, are analyzed. Further, this parallel algorithm is compared to a conventional fixed-population-size genetic algorithm. We observe that the dramatic changes in population size allow evolution to accelerate.     

基于有性繁殖的遗传算法及其应用

该文提出了一种基于有性繁殖的遗传算法。标准遗传算法（SGA）已经被成功地应用到很多进化优化问题上，但是对于复杂的多模态函数寻优时，会出现早熟收敛现象。为了解决这个问题，该文结合自然界最常见的有性繁殖现象，赋予了遗传个体年龄和性别特征，提出了基于有性繁殖的遗传算法的框架以及实现。通过建立年龄和有性遗传进化算子，对不同年龄和不同性别的个体赋予不同的进化控制参数，克服了早熟收敛问题且保持了群体的多样性。对运动模糊图像复原的测试，也证明了该方法的有效性。     

基于进化个体适应值灰度的自适应交互式遗传算法

为将交互式遗传算法成功应用于复杂优化问题,有必要提高交互式遗传算法的性能。提出基于进化个体适应值灰度的交互式遗传算法,该算法采用灰度衡量进化个体的适应值评价不确定性;通过适应值区间的分析,提取反映进化种群分布的信息;基于此,给出了进化个体的交叉和变异概率。将该算法应用于服装进化设计系统,结果表明该算法在每代可以获取更多的满意解。     

基于改进遗传算法的排课问题研究

针对遗传算法在解决排课问题中易陷入局部最优解的缺陷,提出一种改进的遗传算法。在传统遗传算法基础之上,融合模拟退火思想,使交叉得到的子代以一定概率进入下一代,并对传统的基于概率的计算方法进行改进, 编排出优质的课表。实验结果表明改进算法不仅加快了前期进化速度,而且解决了遗传算法后期易陷入局部最优解的缺陷。     

具有年龄结构的遗传算法

该文提出了一种基于年龄结构的遗传算法。标准遗传算法(SGA)已经被成功地应用到很多进化优化问题上，但是对于复杂的多模态函数寻优时，会出现早熟收敛现象。为了解决这个问题，该文结合自然规律和遗传算法的特点，提出了具有年龄结构的遗传算法的框架以及实现。通过对不同年龄个体的不同操作，克服了早熟收敛问题且保持了群体的多样性，使算法能顺利地收敛到全局最优值。     

基于复合域上的椭圆曲线密码体制的计算算法

基于有限域上椭圆曲经公开密钥协议的离散对数计算算法正日益成为热点，其基本的操作是标量乘法：即用一整数乘以椭圆曲线上给定的点P。协议的主要开锁在于椭圆曲线的标量乘操作上，本文给出了3个逄法进行椭圆曲线密码系统的有效计算，第一个算法采用加－减法链的方法处理标量乘法问题；第二个算法给出了正整数n的NAF形式；第三个算法采用窗口的方法处理NAF（n）从而进一步提高加－减法链的效率，这三个算法的有机结合从银大程度上提高了椭圆曲线密码体制的加／解密速度。     

A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding

DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simultaneously several constraints, such as physical, chemical and logical constraints. In this paper, a novel quantum chaotic swarm evolutionary algorithm (QCSEA) is presented, and is first used to solve the DNA sequence optimization problem. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm cannot only avoid the disadvantage of easily getting to the local optional solution in the later evolution period, but also keeps the rapid convergence performance. The simulation results demonstrate that the proposed quantum chaotic swarm evolutionary algorithm is valid and outperforms the genetic algorithm and conventional evolutionary algorithm for DNA encoding.     

类基于蚁群优化的贝叶斯置信网结构学习策略及性能分析*

针对贝叶斯置信网的结构学习问题,提出一种遵循典型ACO算法框架(ACO-TSP)的贝叶斯网结构学习算法（ACO-BN）,并拓展为包括EAS-BN、ACS-BN和MMAS-BN在内的一类算法。用这类算法在若干典型贝叶斯网络结构学习问题上分别与经典贝叶斯网学习算法(K2、B)、用于贝叶斯网学习的通用优化算法（simulated annealing、Tabu searching和genetic searching）以及L. M. de Campos等人提出的基于蚁群优化的贝叶斯网络结构学习算法 Ant-K2SN