支持外观造型创新设计的计算机辅助设计环境

介绍一个计算机辅助设计环境，该环境利用进化计算及可视化技术生成二维草图及三维图像，以支持设计人员的创新设计．用一个艺术设计实例介绍了该环境所采用的基于树结构的遗传算法，实例用一般的数学表达式生成艺术品底座外形，用复合函数表达式生成艺术花卉的三维图像．实例表明：文中方法能够生成一些有创意的解，并展示了进化的计算方法在创新设计中的潜力。     

支持创新设计的多Agent系统

提出了一种支持创新设计的多Agent系统，该系统为设计人员提供有效的辅助工具。多Agent为设计人员的相互协作提供了支撑框架，基于树结构的遗传算法拓宽了设计人员的思维空间。以花瓶设计为例描述了系统的辅助设计过程。该实例利用VC自带的图形函数库OpenGL生成形状草图，用基于特征的产品设计树产生可选的部件组合，用遗传算法实现创新设计。     

自适应协同进化算法及其在创新设计中的应用研究*

提出一种自适应协同进化算法,对其进行了数学描述。设计了一个支持该算法的创新设计系统,为分布式环境下设计人员的协作和创新思路的开拓提供了支撑平台。算法中自适应学习的引入为在设计中自动而有效地使用先验智能提供了可行性。最后以一个建筑实例的设计为例对所述的方法和系统加以描述。     

基于遗传算法的工业以太网环型拓扑优化

为了改善交换式工业以太网的网络性能,给出了其采用环形拓扑时的设计原则,并把它们等价为一个带约束条件的网络优化问题．然后通过设计遗传算法来寻找该优化问题的解,为避免遗传算法的早熟收敛,在进化过程中采用了基于混沌迁移的伪并行进化方案．最后针对一个仿真例子,对比了基于遗传算法进行拓扑优化前后的网络性能,仿真结果验证了该优化方法的有效性．     

药物分子对接中的构象搜索策略

药物分子对接设计是大规模数据库筛选的理想途径。本文在介绍分子对接理论的基础上，建立了一个数学规划模型，将分子对接中的构象搜索转化为约束极小化问题，并采用带有空间收缩的小种群遗传算法进行求解。在遗传算法中还引入了信息熵的概念，用熵控制各种群搜索空间的收缩。本方法用种群的多样性避免了遗传进化的早熟现象，以空间收缩尺度作为停机判据，有效地控制了算法的收敛。在多种群进化机制上，采用小种群策略，极大程度地减少了计算量，提高了分子对接的效率。实例表明本方法适用于药物分子对接设计。     

基于动物群落结构的改进病毒进化遗传算法

基于动物群落组织结构提出了一种改进的病毒进化遗传算法.主种群由一个父亲和若干个母亲及单身汉组成,父亲和母亲交叉产生后代,单身汉变异产生后代.病毒分为大病毒和小病毒,在前期迭代过程中,大病毒起作用,在后期迭代过程中,小病毒起作用.实例证明,改进的病毒进化遗传算法性能优于传统的病毒进化遗传算法.     

支持外观造型创新设计的进化计算方法

采用二叉树结构表示数学函数，并对树结构表示的数学函数实施交叉、变异、算术运算、选择等操作，产生新的后代．数学函数对应的曲线可以形成二维草图，经计算机处理后的三维图，能产生造型各异的组件，把生成的组件和人工设计的组件统一保存到原型实例库中。对组件进行二进制编码，然后执行简单的遗传算法，能生成不同的组合方案．由于利用了CAD的可视化环境，并结合人类设计师的智慧及进化计算技术，因此可以产生很多新颖的外观造型．最后以台灯设计为例，介绍了文中算法的执行过程．     

MATLAB遗传算法工具箱的设计

在简要分析遗传算法要素的基础上，介绍了基于MATLAB的遗传算法工具箱的设计。最后给出了一个用设计的MATLAB遗传算法工具箱的求函数极值的应用实例。     

基于改进遗传算法的最小生成树算法

以图论和改进遗传算法为基础，提出了一种求最小生成树的遗传算法。该算法采用二进制表示最小树问题，并设计出相应的适应度函数、算子以及几种控制策略，以提高执行速度和进化效率。传统算法一次只能得到一个候选解。用该算法对其求解，可以在较短的时间内以较高的概率获得多个候选解。应用实例表明该算法优于传统算法。     

支持进化的协同设计系统研究及实现

提出了一个支持进化的协同设计框架系统clientAgentDcsign1.0,将进化技术和协同技术结合到一起用于新产品的开发。系统利用改进后的遗传算法实现进化设计,多Agcnt则为协同环境提供框架支持。以此系统为基础,以Vc 6．0及实体造型引擎ACIS为工具开发了一个用于建筑设计的协同创新平台HouseDev。     

遗传算法在建筑概念设计中的应用

介绍了一种可以应用于建筑概念设计的遗传算法.该算法采用基于数学表示二叉树结构的编码方法,以及相应的交叉、变异操作,目标函数及人机交互相结合的适应度值确定方法,生成简单的曲线.选定的曲线经过三维可视化处理形成实体.这些生成的三维实体与人工设计的构件一起被分类,然后统一保存到构件库中.通过采用二进制编码的遗传算法,生成组合方案,组合构件库的构件,形成比较复杂的外观造型.以一个建筑外观设计为例,介绍了算法的执行过程.     

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.     

基于云模型的手机轮廓创新进化设计方法研究与实现

随着信息技术的发展,产品的创新性愈显重要。遗传算法已广泛应用于创新设计当中,论文将一种定性概念与定量表示之间的不确定性转换模型——云模型应用于遗传算法的变异操作中,并将该算法用于手机轮廓创新进化设计中。实验证明该算法能克服原有遗传算法的缺点,加快设计速度,拓宽设计思路,能够增强构件概念设计的创新性。     

基于改进的遗传算法的多目标优化问题研究

研究多目标优化算法问题,针对传统的多目标优化算法由于计算复杂度非常高,难以获得令人满意的解等问题,在图论和遗传算法基础上,提出了一种改进的遗传算法求解多目标优化方法。首先采用二进制编码表示最小树问题,然后采用深度优先搜索算法进行图的连通性判断,给出了一种新的适应度函数,以提高算法执行速度和进化效率。最后仿真结果表明,与经典的Prim算法和Kruskal算法相比,新算法复杂度较低,并能在第一次遗传进化过程中获得一批最小生成树,适合于解决不同类型的多目标最小树问题。     

Extraction and reuse of design patterns from genetic algorithms using case-based reasoning

In this paper, we propose a case-based reasoning scheme in which we extract domain knowledge (in the form of design patterns) from a genetic algorithm used to optimize combinational logic circuits at the gate level. Such information is used in two ways: first, we show how the selection pressure of the genetic algorithm is biased by Boolean simplification rules that are normally adopted by human designers, including some which are not completely straightforward. Secondly, we reuse some of these design patterns extracted from the evolutionary process to reduce convergence times of a genetic algorithm using previously found solutions as cases to solve similar problems.The second author acknowledges support from CONACyT through project No. 32999-A. The third author acknowledges partial support for this work through CONACyT Project No. I-39324-A.     

Improved genetic algorithm for generalized transportation problem

In this paper, we introduce the genetic algorithm approach to the generalized transportation problem (GTP) and GTP with a fixed charge (fc-GTP). We focus on the use of Prüfer number encoding based on a spanning tree, which is adopted because it is capable of equally and uniquely representing all possible trees. From this point, we also design the criteria by which chromosomes can always be converted to a GTP tree. The genetic crossover and mutation operators are designed to correspond to the genetic representations. With the spanning-tree-based genetic algorithm, less memory space will be used than in the matrix-based genetic algorithm for solving the problem; thereby computing time will also be saved. In order to improve the efficiency of the genetic algorithm, we use the reduced cost for the optimality of a solution and the genetic algorithm to avoid degeneration of the evolutionary process. A comparison of results of numerical experiments between the matrix-based genetic algorithm and the spanning-tree-based genetic algorithm for solving GTP and fc-GTP problems is given. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

A study on a cooperative character modeling based on an improved NSGA II

Different animation characters are loved by people at different ages, in different countries and from different background, which implies the difficulties of designing characters that might be loved by many. Grouping different designers together by computer networks or the Internet is surely a good solution to design a character with expected popularity. This paper presents a character modeling method based on an improved non-dominated sorting genetic algorithm II (CMIN). CMIN borrows ideas from biological evolution, especially from multi-objective genetic algorithm (MOGA) and is formalized as a procedure for character modeling. CMIN adopts binary tree data structure to express transformation rules which are used to diversify character models and uses crossover and mutation operators of genetic algorithm to generate new rules. CMIN also adopts cooperative multi-objective evaluation on generated characters. The objectives are designed to embody both qualitative and quantitative aspects of character personalities, which are assigned by different cooperative designers and calculated automatically by computers respectively. The incorporation of qualitative and quantitative evaluation is formally realized by introducing a MOGA framwork. A multi-objective evaluation-based cooperative character modeling system (MOCMS) was developed to verify the proposed CMIN. Representative case studies demonstrate that the proposed method can evolve character models according to the designers’ intentions and preferences and generate creative character models far beyond man’s own imagination.     

An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing

This paper proposes a new optimal Latin hypercube sampling method (OLHS) for design of a computer experiment. The new method is based on solving sequencing and continuous optimisation using simulated annealing. There are two sets of design variables used in the optimisation process: sequencing and real number variables. The special mutation operator is developed to deal with such design variables. The performance of the proposed numerical strategy is tested and compared with three established OLHS methods, namely genetic algorithm (GA), enhanced stochastic evolutionary algorithm (ESEA) and successive local enumeration (SLE). Based on 30 test problems with various design dimensions and numbers of sampling points, the proposed method gives the best results. The method can generate an optimum set of sampling points within reasonable computing time; therefore, it can be considered as a powerful tool for design of computer experiments.