Genetic Programming and Autoconstructive Evolution with the Push Programming Language

Push is a programming language designed for the expression of evolving programs within an evolutionary computation system. This article describes Push and illustrates some of the opportunities that it presents for evolutionary computation. Two evolutionary computation systems, PushGP and Pushpop, are described in detail. PushGP is a genetic programming system that evolves Push programs to solve computational problems. Pushpop, an autoconstructive evolution system, also evolves Push programs but does so while simultaneously evolving its own evolutionary mechanisms.     

程序设计语言的演进

本文对程序设计语言的发展演进特征进行了分析，着重分析了比较了目前流行的面向对象程序设计语言。     

基于遗传规划技术的碳势测定新方法

本文阐述了遗传规划的原理和技术．提出一种基于遗传规划技术的碳势测定新方法。该方法不依赖于问题域，无需对输入的数据进行预处理，可作为解决相关问题的通用方法。该方法精度高，成本低，利于在线检测与控制，可以获得显性的预测函数表达式，能够处理复杂的非线性问题。     

基于遗传规划技术的碳势测定新方法

本文阐述了遗传规划的原理和技术,提出一种基于遗传规划技术的碳势测定新方法。该方法不依赖于问题域,无需对输入的数据进行预处理,可作为解决相关问题的通用方法。该方法精度高,成本低,利于在线检测与控制,可以获得显性的预测函数表达式,能够处理复杂的非线性问题。     

基于遗传规划的哈默斯坦模型的辨识

该文基于遗传规划提出了一种辨识哈默斯坦模型的新方法。哈默斯坦模型由静态非线性模块和动态线性模块串联而成,因此系统辨识的目标是要找到非线性和线性模块的最优数学模型。该文通过遗传规划确定非线性模块的函数结构,并结合遗传算法确定模型的未知参数,适应度值的计算采用了最小信息量准则(A IC),以平衡模型的复杂度和精确度。该方法不需要对模型的先验知识有详细了解,就能达到较好的辨识效果,并且能够克服观测噪声的污染,获得参数的无偏估计。仿真结果说明了该方法的有效性。     

用遗传算法求解的非线性规划问题

针对用遗传算法求解非线性规划问题时交叉、变异过程中所产生的新一代近似解合法性问题,提出了预先定界的方法,根据约束条件确定下一代近似解的合法的取值范围。实验证明该方法有效,且能保证在较短的时间里取得较好的结果。     

并行程序设计语言发展现状

In this paper we introduce the history of the parallel programming language and list some of currently parallel programming languages. Then according to the classified principle. We analyze some of the representative parallel programming languages in detail. Finally, we show a further feature to the parallel programming language.     

用AVR汇编语言实现AES及其优化

AES是美国高级加密标准算法,将在未来几十年里代替DES在各个领域中得到广泛应用。本文在研究分析AES加密算法原理的基础上,着重说明算法的实现步骤,并结合AVR汇编语言完整地实现AES加密和解密。根据AES原理,提出几种列变化的优化算法,并根据实验结果分析和比较它们的优缺点。     

Robust Text-Independent Speaker Verification Using Genetic Programming

Robust automatic speaker verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, genetic programming offers inherent feature selection and solutions that can be meaningfully analyzed, making it well suited to this task. This paper introduces a genetic programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. We also show the effect of a simulated telephone network on classification results which highlights the principal advantage, namely robustness to both additive and convolutive noise     

基于遗传规划的多光谱图像分类

针对多光谱图像分类这一多类别模式识别问题,将二进制纠错编码与GP(GeneticProgramming)算法相结合,并用改进后的编码矩阵代替原先的二进制编码矩阵对图像进行分类,从而建立了新的基于GP的多光谱图像分类算法,给出了用该方法对多光谱图像中地物进行分类的实例。结果表明与以往基于GP的分类方法相比,该文方法体现出较高的分类性能,为遗传规划在多类别模式识别问题中的应用提供了又一条可行的途径。     

递归算法在C语言程序设计中的实现

本文根据递归算法的定义,对其在C语言程序设计中的应用进行了阐述,通过对递归的内部实现过程的描述,对递归的使用进行评价,说明递归在程序设计中具有一定的使用空间.     

递归算法在C语言程序设计中的实现

本文根据递归算法的定义，对其在C语言程序设计中的应用进行了阐述，通过对递归的内部实现过程的描述，对递归的使用进行评价，说明递归在程序设计中具有一定的使用空间．     

Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming

This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.     

基于GNP算法类人机器人全身运动的规划

介绍了一种基于遗传网络算法(Genetic Network Programming,以下简称GNP)的类人机器人全身运动规划方法。在介绍了GNP算法的核心理念之后,将着重阐述GNP遗传网络的构建方案以及如何将其应用到类人机器人的全身运动规划中,并在论文最后分析实验数据和仿真结果。     

“高级语言程序设计”网络课程的研究与开发

随着现代教育技术的发展,通过网络呈现教学内容、实施教学活动的网络课程成为现代教育改革的重要内容。以"高级语言程序设计"网络课程的开发与实现为例,探讨该网络课程的意义、目标和教学内容设计方案,总结开发网络课程的过程。     

Implementing Coloured Petri Nets Using a Functional Programming Language

Coloured Petri Nets (CPNs) are a graphically oriented modelling language for concurrent systems based on Petri Nets and the functional programming language Standard ML. Petri Nets provide the primitives for modelling concurrency and synchronisation. Standard ML provides the primitives for modelling data manipulation and for creating compact and parameterisable CPN models.Functional programming and Standard ML have played a major role in the development of CPNs and the CPN computer tools supporting modelling, simulation, verification, and performance analysis of concurrent systems. At the modelling language level, Standard ML has extended Petri Nets with the practical expressiveness required for modelling systems of the size and complexity found in typical industrial projects. At the implementation level, Standard ML has been used to implement the formal semantics of CPNs that provide the theoretical foundation of the CPN computer tools.This paper provides an overview of how functional programming and Standard ML are applied in the CPN modelling language and the supporting computer tools. We give a detailed presentation of the key algorithms and techniques used for implementing the formal semantics of CPNs, and we survey a number of case studies where CPNs have been used for the design and analysis of systems. We also demonstrate how the use of a Standard ML programming environment has allowed Petri Nets to be used for the implementation of systems.     

C/C 中宏的编制和使用

目前 ,C和C 语言得到了广泛的运用。宏的编制和使用属于其中的高级编程主题。本文结合作者的编程体会 ,论述了宏的编制和使用的具体方法。     

Detecting New Forms of Network Intrusion Using Genetic Programming

How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on networks is presented and four genetic operators, namely reproduction, mutation, crossover, and dropping condition operators, are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that a rule generated by GP has a low false positive rate (FPR), a low false negative rate and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate with low FPR. An alternative to the DARPA evaluation approach is also investigated.     

并发成员系统程序设计语言环境下的机器人协同问题求解

本文首先提出并证明了模型世界下双机器人协同问题求解的难解性，然后介绍了已开发成功的支持多种特别是协同问题求解的并发成员系统程序设计语言ＭＳ－１在该问题领域的成功应用。