测试集自动生成工具TUGEN的设计与实现

测试集自动生成工具的研究是协议一致性测试领域中比较活跃的一个分支，本文在对目前已有的各种测试集生成方法进行分析的基础上，提出了一种新的测试集自动生成方法并对它的实现ＴＵＧＥＮ作了介绍．ＴＵＧＥＮ基于一种称为ＥＢＥ的形式模型，ＥＢＥ模型只对协议的外部行为进行描述，而且可以从协议的Ｅｓｔｅｌｌｅ或ＬＯＴＯＳ描述中转化得到．ＴＵＧＥＮ以协议的ＥＢＥ－ＮＦ描述作为输入，使用我们新提出的一套测试事例生成策略，最后产生出ＴＴＣＮ．ＭＰ格式的测试集．我们用Ｘ．２５ＬＡＰＢ协议的ＥＢＥ－ＮＦ描述作为例子，对ＴＵＧＥＮ     

如何实现Foxpro和Sybase之间的数据传输

本文讲述了Ｓｙｂａｓｅ和Ｆｏｘｐｒｏ之间的数据传输问题，重点介绍了使用Ｂｃｐ．ＣｏｐｙＴｏ命令及Ｅｄｉｔ软件将Ｆｏｘｐｒｏ数据库内容复制到Ｓｙｂａｓｅ数据表的实现方法     

Visual Foxpro多媒体信息管理程序设计

本文介绍利用ＭＳＯＬＥ２．０技术将Ｖｉｓｕａｌ Ｂａｓｉｃ４．０中ＭＣＩ控件移植到Ｖｉｓｕａｌ Ｆｏｘｐｒｏ系统中的方法和步骤，实现了使用Ｖｉｓｕａｌ Ｆｏｘｐｒｏ开发多媒体信息管理程序。     

知识概念的模糊模型及模糊目标的识别

作为ＥＢＬ（Ｅｘｐｌａｎａｔｉｏｎ－ＢａｅｓｅｄＬｅａｒｎｉｎｇ）的发展,从概念的自然形成过程出发,提出了一种新的概念模型ＦＥＢＭ（ＦｕｚｚｙＥｘｐｌａｎａｔｉｏｎ－ＢａｓｅｄＭｏｄｅｌ）当概念的解释谓词集中模糊集以及解释谓词取模糊逻辑值时,给出了求概念真值的表达式;为了解决模糊概念的识别问题,引入了概念的模糊解释树ＦＥＴ,接着给出了对象的模糊识别算法ＦＥＢＬ,最后讨论了ＦＥＢＭ与ＦＥＢＬ的可操作     

基于BDI模型构建信息Agent

本文提出一种用于分布式多Ａｇｅｎｔ环境的以ＢＤＩ模型为基础构建信息Ａｇｅｎｔ的方法,给出了ＰＥＲＦＯＲＭ（ＰＥＲｓｏｎａｌ ｉｎＦＯＲｍａｔｉｏｎ Ｍａｎａｇｅｒ）系统的模型和算法。     

IDEF4与面向对象设计方法

ＩＤＥＦ方法是由美国空军并行工程信息集成项目的Ａｒｍｓｔｒｏｎｇ实验室研制的，ＩＤＥＦ４是其家族的一员。本文以ＩＤＥＦ４为主探索了各主要面向对象设计方法的特点，并对ＩＤＥＦ４方法的优缺点作了简要分析，旨在本文能作为研制一种实用的面向对象设计方法的基础，为设计面向对象计算机辅助软件设计工具提供参考。     

特殊索引文件的建立

本文基于ｘＢＡＳＥ关系数据库管理系统（包括ｄＢＡＳＥⅢ（ｐｌｕｓ），ＦｏｘＢＡＳＥ，ＦｏｘＰｒｏ等），介绍了如何通过各种上数据库函数建立特殊索引文件的方法。     

XBASE数据库文件与ORACLE之间的数据交换

本文介绍了Ｆｏｘｂａｓｅ、Ｆｏｘｐｒｏ等ＸＢＡＳＥ数据库中的数据文件同ＯＲＡＣＬＥ之间数据交换的方法。首先介绍如何使用ＳＱＬ＊ＵＬＯＡＤＥＲ将ＸＢＡＳＥ数据库的数据转储到ＯＲＡＣＬＥ数据库中。由于ＯＲＡＣＬＥ没有提供将ＯＲＡＣＬＥ数据库中的数据传储到ＸＢＡＳＥ数据库文件的工具，因此，本文然后给出了解决这一问题的方法。     

用户界面的可变状态模型及实现

ＯＰＥ模型是一种较好的用户界面模型，它用面对象的方法将弹出式状态转换网络模型和事件处理器模型有机地结合起来，使用户界面的设计，实现变得相当规范和容易，本文在简单地介绍了ＯＰＥ模型后，针对ＯＰＥ在描述层菜单时需要较多的状态转换规则的问题，提出了可变状态的概念，并将此概念了引入了微机版ＵＩＭＳ系统－Ｆａｓｔｏｏｌ中（Ｆａｓｔｏｏｌ是ＯＰＥ模型一个特例的实现），使Ｆａｓｔｏｏｌ在被用来描述层次菜单时更简     

Windows2000回收站使用技巧

笔者撰写本文的首要原则是,在不需要进入注册表的情况下．尽量不进入注册表进行修改。０如何从桌面上隐藏“回收站” 如果想从桌面删除“回收站” 你可以打开注册表,找到主键; ＲＥＧＥＤＩＴ ［ＨＫＥＹ_ＬＯＣＡＬ_ＭＡＣＨＩＮＥ＼ＳＯＦＴＷＡＲＥ＼Ｍｉｃｒｏｓｏｆｔ＼Ｗｉｎｄｏｗｓ＼ＣｕｒｒｅｎｔＶｅｒｓｉｏｎ＼Ｅｘｐｌｏｒｅｒ＼Ｄｅｓｋｔｏｐ＼ＮａｍｅＳｐａｃｅ＼｛６４５ＦＦ０４０－５０８１－１０１Ｂ－９Ｆ０８－００ＡＡ００２Ｆ９５４Ｅ｝］ 首先为了安全,需要把它备份下来,就是单击该主键,选【注册表】中【…     

An introduction to simulated evolutionary optimization

Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed.     

A new model of simulated evolutionary computation-convergenceanalysis and specifications

There have been various algorithms designed for simulating natural evolution. This paper proposes a new simulated evolutionary computation model called the abstract evolutionary algorithm (AEA), which unifies most of the currently known evolutionary algorithms and describes the evolution as an abstract stochastic process composed of two fundamental operators: selection and evolution operators. By axiomatically characterizing the properties of the fundamental selection and evolution operators, several general convergence theorems and convergence rate estimations for the AEA are established. The established theorems are applied to a series of known evolutionary algorithms, directly fielding new convergence conditions and convergence rate estimations of various specific genetic algorithms and evolutionary strategies. The present work provides a significant step toward the establishment of a unified theory of simulated evolutionary computation     

Evolutionary model for the Steiner tree problem with flow-dependent weights

An evolutionary model developed for solving the Steiner tree problem with flow-dependent weights is discussed. The solution search is simulated as an evolutionary process at two interconnected levels, accidental speciation and the evolution of individual-species populations. For this purpose, original genetic operators are applied.     

基于网络同步的链路预测连边机理分析研究

链路预测是研究复杂网络结构演化趋势的重要组成部分, 用于预测网络丢失的连边和未来可能出现的连边, 具有极大的理论和应用价值.当前链路预测研究成果主要基于网络结构特征对连边进行预测, 具体分析其连边机理的研究较少.网络同步的研究能够深刻反映节点的动力学演化行为与网络结构之间的内在机理.本文针对链路预测考虑的静态网络引入节点动力学模型构成动态网络, 通过分析链路预测连边与动态网络模型同步之间的关系, 对链路预测连边机理进行分析研究.通过实验与理论分析总结发现了链路预测连边具有同步能力稳定性的规律.进一步讨论了链路预测连边的动力学机理, 并揭示了链路预测连边机理与真实网络演化的差别.     

Structured synthesis of MEMS using evolutionary approaches

In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical synthesis process for MEMS. The paper first introduces the flow of a structured MEMS design process and emphasizes that system-level lumped-parameter model synthesis is the first step of the MEMS synthesis process. At the system level, an approach combining bond graphs and genetic programming can lead to satisfactory design candidates as system-level models that meet the predefined behavioral specifications for designers to trade off. Then at the physical layout synthesis level, the selection of geometric parameters for component devices and other design variables is formulated as a constrained optimization problem and addressed using a constrained genetic algorithm approach. A multiple-resonator microsystem design is used to illustrate the integrated design automation idea using these evolutionary approaches.     

Application of the utility function method for behavioral organization in a locomotion task

The generation of a complete robotic brain for locomotion based on the utility function (UF) method for behavioral organization is demonstrated. A simulated, single-legged hopping robot is considered, and a two-stage process is used for generating the robotic brain. First, individual behaviors are constructed through artificial evolution of recurrent neural networks (RNNs). Thereafter, a behavioral organizer is generated through evolutionary optimization of utility functions. Two systems are considered: a simplified model with trivial dynamics, as well as a model using full newtonian dynamics. In both cases, the UF method was able to generate an adequate behavioral organizer, which allowed the robot to perform its primary task of moving through an arena, while avoiding collisions with obstacles and keeping the batteries sufficiently charged. The results for the simplified model were better than those for the dynamical model, a fact that could be attributed to the poor performance of the individual behaviors (implemented as RNNs) during extended operation.     

进化编程优化RBF神经网络的结构和参数

本文利用进化编程（ＦＰ）来同时进化径向基函数神经网络（ＲＢＦＮＮ）的结构和参数。与其它进化神经网络方法有以下四个方面的不同：（１）ＥＰ是基于拉马克的进化学说,强调父代与子代之间的行为联接;（２）进化算子中仅有突变,而没有交叉,以消除互换问题;（３）突变操作中,删除总是先于添加进行,以获得最简的网络结构;（４）利用测试样本集构造适应度函数,以提高网络的泛化能力。用进化ＲＢＦＮＮ来预测Ｍａｃｋｅｙ－Ｇ     

自律个体的一种遗传强化模型研究

自律个体的遗传强化模型是模拟实际生物进化机制的计算模型。本文利用进化算法和人工神经网络的研究方法,设计一种自律个体的遗传强化模型。该模型强调多层次学习,实现了先天的遗传学习进化和后天的个体神经系统学习进化的有机结合。本文同时将该模型应用于模拟机器人的生存控制,观察它在环境中的行为表现及经能力,取得了满意的实验结果。     

Bayesian network modeling for evolutionary genetic structures

Evolutionary theory states that stronger genetic characteristics reflect the organism’s ability to adapt to its environment and to survive the harsh competition faced by every species. Evolution normally takes millions of generations to assess and measure changes in heredity. Determining the connections, which constrain genotypes and lead superior ones to survive is an interesting problem. In order to accelerate this process,we develop an artificial genetic dataset, based on an artificial life (AL) environment genetic expression (ALGAE). ALGAE can provide a useful and unique set of meaningful data, which can not only describe the characteristics of genetic data, but also simplify its complexity for later analysis.To explore the hidden dependencies among the variables, Bayesian Networks (BNs) are used to analyze genotype data derived from simulated evolutionary processes and provide a graphical model to describe various connections among genes. There are a number of models available for data analysis such as artificial neural networks, decision trees, factor analysis, BNs, and so on. Yet BNs have distinct advantages as analytical methods which can discern hidden relationships among variables. Two main approaches, constraint based and score based, have been used to learn the BN structure. However, both suit either sparse structures or dense structures. Firstly, we introduce a hybrid algorithm, called “the E-algorithm”, to complement the benefits and limitations in both approaches for BN structure learning. Testing E-algorithm against a standardized benchmark dataset ALARM, suggests valid and accurate results. BAyesian Network ANAlysis (BANANA) is then developed which incorporates the E-algorithm to analyze the genetic data from ALGAE. The resulting BN topological structure with conditional probabilistic distributions reveals the principles of how survivors adapt during evolution producing an optimal genetic profile for evolutionary fitness.     

Real-time deterministic chaos control by means of selected evolutionary techniques

This contribution introduces continuation of an investigation on deterministic spatiotemporal chaos real-time control by means of selected evolutionary techniques. Real-time-like behavior is specially defined and simulated with the spatiotemporal chaos model based on mutually nonlinearly joined n equations, so-called coupled map lattices (CML). Four evolutionary algorithms are used for chaos control here: differential evolution, self-organizing migrating algorithm, genetic algorithm and simulated annealing in a total of 12 versions. For modeling of real-time spatiotemporal chaos behavior, the so-called CML were used based on logistic equation to generate chaos. The main aim of this investigation was to show that evolutionary algorithms, under certain conditions, are capable of real-time control of deterministic chaos, when the cost function is properly defined as well as parameters of selected evolutionary algorithm. Investigation consists of four different case studies with increasing simulation complexity. For all algorithms, each simulation was repeated 100 times to show and check robustness of used methods. All data were processed and used in order to get summarizing results and graphs.