A new programming paradigm for engineering design software

Currently available programming and database systems are insufficient for engineering applications. The authors contend that a logical progression from a formal conceptual model of the engineering domain to a computational model will lead to new programming paradigms capable of directly supporting engineering applications in a rigorous, concise manner. A formal domain model devised by the authors, theHybrid Model (HM) of design information, is briefly introduced. It is an extension of axiomatic set theory and is discussed in detail elsewhere. HM forms the basis ofDesigner, a prototype-based object-oriented programming language supporting a signature-based canonical message-passing mechanism and multiple inheritance. Designer is implemented using the Scheme programming language. Because Designer satisfies a formal conceptual model, and because it is based on a formally specified language, its robustness and logical validity are superior to those of other languages not founded on formal principles. Designer combines concepts of functional and object-oriented programming to provide the formal rigor and flexibility to capture the complex and strongly interrelated information that designers use. Examples demonstrate how Designer represents design information. The results of the authors' research indicate that Designer can capture design information (including aspects of functional requirements and design intent) effectively and efficiently.     

Mathematical programming models and algorithms for engineering design optimization

Mathematical programming provides general tools for engineering design optimization. We present numerical models for simultaneous analysis and design optimization (SAND) and multidisciplinary design optimization (MDO) represented by mathematical programs. These models are solved with numerical techniques based on the feasible arc interior point algorithm (FAIPA) for nonlinear constrained optimization. Even if MDO is a very large optimization problem, our approach reduces considerably the computer effort. Several tools for very large problems are also presented. The present approach is very strong and efficient for real industrial applications and can easily interact with existing simulation engineering codes.     

基于改进型基因表达式编程的神经网络优化设计

提出一种用基因表达式编程(GEP)自动设计神经网络的算法.针对标准GEP算法在优化神经网络过程中的早熟现象和变异率低问题,对算法进行了改进,并给出算法的具体应用实例.与其它优化算法的对比实验表明,GEP是一种有效的神经网络设计方法,并且改进的GEP算法比标准GEP算法进化效率高,将收敛率提高了37个百分点,收敛速度快,进化代数仅是标准算法的58%.     

多表达式程序设计的新型评估方法

多表达式程序设计(MEP)是应用十分广泛的自动程序设计方法。从MEP的染色体表示规则及种群演化方式来看,每个染色体中的任何基因都有可能多次被当前或其它后续种群中的其他基因引用,从而造成重复计算,耗费大量时空资源。由此提出并实现了一种新型评估方法,该新型评估方法在不改变传统MEP的染色体表示规则和种群演化方式的情况下,能够准确有效地识别演化过程中所有被重复引用的基因,从而避免了大量重复计算,显著提高了演化效率。     

开放尾部的基因表达式程序设计

基因表达式程序设计（GEP）的染色体由具有特殊限制的头、尾组成,并要求尾部符号严格取自基本的终端集。这一做法作用明了、易于表述,基本为现有GEP所采纳,但不利于语义计算的重用。谋求突破尾部限制条件,探究一种开放尾部的新型GEP算法。该算法将运行过程产生的优良个体动态地引入种群个体的基因,从而实现运算精度的提升。符号回归实验表明,开放尾部的GEP算法在平均精度性能上要优于主流GEP方法。     

多细胞基因表达式编程的函数优化算法

针对处理复杂的函数优化问题时传统演化算法易出现收敛性能不佳、搜索冗长和精度不高等问题,提出了一种基于多细胞基因表达式编程的函数优化新算法.该算法引入了同源基因和细胞系统思想,设计了相应新的个体编码方案、种群生成和遗传操作策略.通过对8个Benchmarks函数的对比实验,验证了该算法具有很强的全局寻优能力、较佳的收敛性能和更高的解精度.     

Fitness approximation for bot evolution in genetic programming

Estimating the fitness value of individuals in an evolutionary algorithm in order to reduce the computational expense of actually calculating the fitness has been a classical pursuit of practitioners. One area which could benefit from progress in this endeavour is bot evolution, i.e. the evolution of non-playing characters in computer games. Because assigning a fitness value to a bot (or rather, the decision tree that controls its behaviour) requires playing the game, the process is very costly. In this work, we introduce two major contributions to speed up this process in the computer game Unreal Tournament 2004?. Firstly, a method for fitness value approximation in genetic programming which is based on the idea that individuals that behave in a similar fashion will have a similar fitness. Thus, similarity of individuals is taken at the performance level, in contrast to commonly employed approaches which are either based on similarity of genotypes or, less frequently, phenotypes. The approximation performs a weighted average of the fitness values of a number of individuals, attaching a confidence level which is based on similarity estimation. The latter is the second contribution of this work, namely a method for estimating the similarity between individuals. This involves carrying out a number of tests consisting of playing a ‘static’ version of the game (with fixed inputs) with the individuals whose similarity is under evaluation and comparing the results. Because the tests involve a limited version of the game, the computational expense of the similarity estimation plus that of the fitness approximation is much lower than that of directly calculating the fitness. The success of the fitness approximation by similarity estimation method for bot evolution in UT2K4 allows us to expect similar results in environments that share the same characteristics.     

基因表达式编程的一种新遗传算子

遗传算子在演化算法中发挥重要的作用。设计了基因表达式编程中的一种新遗传算子（TIS）,它在基因尾部随机选择一个小片段,然后复制该片段（或该片段的倒置）并插入到基因尾部的任意位置,插入点后的符号依次向后挪动,超过尾部长度的编码被丢弃。实验表明,该算子在挖掘多元函数时能提高算法的成功率和收敛速度。     

Object-oriented programming for engineering applications

The object-oriented programing (OOP) methodology is introduced as a means for improving the management of a complex engineering software. The drawbacks of existing engineering software development techniques are pinpointed. The basic concepts of the OOP methodology are interpreted in the context of engineering applications. The familiar concept of a matrix is used as an example to interpret the ideas presented. It is concluded that the OOP methodology facilitates the management of complex engineering software systems.     

Application of gene expression programming in hot metal forming for intelligent manufacturing

Design of the die in hot metal forming operations depends on the required forming load. There are several approaches in the literature for load prediction. Artificial neural networks (ANNs) have been successfully used by a few researches to estimate the forming loads. This paper aims at using the effectiveness of a new evolutionary approach called gene expression programming (GEP) for the estimation of forging load in hot upsetting and hot extrusion processes. Several parameters such as angle (α), L/D ratio (R), friction coefficient (µ), velocity (v) and temperature (T) were used as input parameters. The accuracy of the developed GEP models was also compared with ANN models. This comparison was evidenced by some statistical measurements (R ², RMSE, MAE). The outcomes of the study showed that GEP can be used as an effective tool for representing the complex relationship between the input and output parameters of hot metal forming processes.

     

基于基因表达式编程的错误定位方法

错误定位是软件调试中最重要且最耗时的部分,错误定位中的任何改进都可以大大降低软件成本,而其中秩函数的选择问题则尤为关键。结合基因表达式编程技术以及基于频谱的错误定位算法,找到适应程序的高效秩函数,提出了一种新的错误定位方法。从程序测试用例的覆盖信息中提取出四种类型的子集信息;通过基因表达式编程训练出适应程序的最优秩函数;利用秩函数计算出每条语句的可疑度值,并按照可疑度值由高到低的顺序逐条检查程序的可疑语句进行错误定位。通过实验,将训练出的秩函数与已经提出的秩函数（如Tarantula,Ochiai等）进行比较分析,结果表明,基于基因表达式编程的错误定位方法具有更精确的错误定位效果和更显著的定位效率。     

基于基因表达式编程的规则分类

为了提高基于规则的分类法中挖掘规则的效率,提出了将基因表达式编程用于挖掘规则的分类方法.针对规则分类问题,设计出了一种新形式的染色体终端符号,引入规则的正确率作为适应度函数度量;将适应度由高到低排序,建立备选规则集;通过使用基因表达式编程挖掘Monk与Acute Inflammations中的规则,利用挖掘出的规则对数据集进行分类.实验结果表明了基于基因表达式编程的挖掘规则分类算法的准确率会高于传统分类算法.     

基因表达式编程算法的改进及其应用研究

基因表达式编程（GEP）算法采用简单编码方式解决了复杂的公式发现问题。本文分析了基本的GEP算法中关键参数、常数集、符号集等设置对公式发现的影响和规律,提出了GEP算法的改进方法,并将其应用在隧道工程领域,得到了双圆盾构施工横向和纵向的地面沉降预测公式,与实测值的比较表明所发现的公式有很好的吻合度。     

基因表达式编程的理论研究综述*

对新兴的基因表达式编程算法在国内外的理论研究现状进行了系统的研究,剖析了GEP的基本原理和特点,并对国内外关于GEP理论和算法的研究现状进行系统的讨论,最后根据笔者的见解探讨了未来研究的重点方向。     

A programming language for engineering computations

C-Map was initially developed for computer-aided instruction in structural analysis and design; however, its present capability extends far beyond, to cover a broad spectrum of numerical methods for engineering computations ranging from elementary calculus to nonlinear constrained optimization problems. The flexibility and power of C-Map derive from the technique of recursive nesting of functions which releases the power of previously isolated, yet robust, algorithms. Such capability far surpasses that of the most comprehensive collection of FORTRAN scientific subroutine package. C-Map comprises four integrated components: (i) a full-screen text editor with multiple stacked windows for simultaneous editing of programs and output files; (ii) a simplified C-like programming language with the basic control structures do-while, while, for, if/else, recursive functions, math library, built-in functions for advanced engineering computations including matrix operations for structural analysis; (iii) an interactive expression evaluator; and (iii) a simple authoring system with automatic cross-referencing of key-words. The program's versatility helps to meet the different needs of users at different stage of learning. A beginner may use only the built-in functions for math and matrix operations to get immediate, useful results; and the advanced users may write their own functions for more complex problems. This paper discusses the features of C-Map and its applications in teaching structural engineering.     

Object-oriented programming for engineering software development

The representation of engineering systems in a manner suitable for computer processing is an important aspect of software development for computer aided engineering. The process of abstraction is a well-known technique for developing data representations. Objects are a mechanism for representing data using abstraction, and object-oriented languages are languages for writing programs to manipulate objects. The paper shows through examples the advantages of object-oriented programming for developing engineering software. Mathematical graphs are used as an abstraction for two problems: (1) sorting activities in a schedule and (2) ordering nodes and elements in a finite element mesh. Classes of objects are developed for generic graphs, activity procedence graphs, and graphs of elements meshes. Object-oriented program development leads to modular programs and a substantial reuse of code for the two problems.     

改进的基因表达式程序设计及其在函数建模中的应用

该文提出了一种改进的基因表达式程序设计的遗传进化算法PGEP，新的算法引入三个算子：（1）基于精英保存策略的精英子空间算子；（2）基于全局收敛策略的变重组、变换概率Pc和变变异概率Pm算子；（3）基于群体搜索技术的变维子空间算子。将改进的基因表达式程序设计应用于函数建模，获得满意的结果。     

基因沉默机制的基因表达式编程

基因表达式编程（Gene Expression Programming,GEP）对多项式函数为目标的符号回归问题计算效果良好,而对包含多种运算目数、非多项式函数的计算效果欠佳。受转基因生物工程中基因沉默现象的启发,提出一种GEP拓展算法SFGEP（Gene Expression Programming of Symbol Field,SFGEP）。SFGEP染色体由表达因子域与表达基因域组成,按“深度优先”原则解释染色体,利用不同操作符目数,形成基因表达的抑制因子和位置效应,实现染色体解释中基因沉默的机制。实验结果表明,相较传统多基因染色体GEP,SFGEP既保持了一定多项式函数挖掘的能力,又在包含不同运算目数操作符的非多项式函数挖掘方面具有更好的效能,SFGEP的成功率更高、收敛速度更快。     

基因表达式编程在环氧酶抑制剂定量构效关系中的应用

基因表达编程方法(GEP)是1种较新的进化计算算法,是1种较好的数据挖掘和建模工具。因其出色的数据挖掘能力并能以数学模型表达数据关系而广受关注,但在化学领域的应用还较少。本文应用GEP方法研究环氧酶抑制剂活性和选择性的构效关系(QSAR),选择变量和建立模型,同时与BP人工神经网络(BP-ANN)和遗传算法-多元线性回归(GA-MLR)方法比较。结果发现,GEP方法的预测较好,且模型稳定。研究显示GEP在定量构效关系研究中,具有广阔的应用前景。     

基于基因表达式编程的TSP问题求解

利用遗传算法求解组合优化问题时,需要特有的遗传算子,才能在候选解空间中有效搜索和进化。基因表达式编程（GEP）是进化计算家族的新成员。旅游商问题（TSP）是典型的组合优化问题,得到了广泛的研究,它的研究成果将对求解NP类问题产生重要影响。基于基因表达式编程（GEP）来解决TSP问题,引入适用组合优化的遗传算子：逆串,基因串的删/插等,最后进行了实验,展示GEP解决TSP问题的方法。实验表明GEP能有效解决TSP问题,设计的系统是强壮健康,其求解速度快且解的质量好。