用混合式遗传算法进行给水管网现状分析

将广义简约梯度法(GRG)思想应用于传统遗传算法,提出一种新的混合式遗传算法(HGA),此方法具有很强的全局和局部搜索能力,并且无需复杂的编码、解码过程,可以准确解决多变量、多峰值的给水管网现状分析问题。采用此算法,绝大多数的计算值与实测值的误差可减小到1%以下,能准确反映管网的实际工况。     

Dynamic systems modelling using genetic programming

In this contribution genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon (Int. J. Control 44 (1986) 235) were used in this work.     

精确实验数据曲线方程的求解

针对精确实验数据的曲线拟合方程化处理方法已不再适应高精度测试技术的问题,提出了用三次样条插值函数进行曲线方程化处理的新方法,并创建了用MATLAB语言编写遗传算法程序进行曲线斜率求解的方法,通过实例表明,采用遗传算法求解出的三次样条插值函数非常适合精确实验数据的曲线方程化处理。     

On the design of an ECOC-Compliant Genetic Algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.     

Optimization of membrane gas separation systems using genetic algorithm

Genetic algorithm is applied for the optimization of the membrane gas separation systems. Air separation for enriched oxygen production is the selected system for investigation. Optimizations for single and triple objective functions are studied. The optimization problem involves the selection of the optimal system configurations from three alternatives, including continuous membrane column (CMC), single stripper permeator (SSP), and two stripper in series permeator (TSSP), as well as the optimal operating conditions. Models of the three configurations and the genetic algorithm procedure are computerized. The objective functions discussed are the Rony separation index, power consumption per unit equivalent pure oxygen, and the membrane area. Both high-pressure and low-pressure (vacuum) operation modes are optimized and the effects of different oxygen product purity and feed rate are analyzed. For single objective function optimization, the solutions obtained using genetic algorithm are slightly inferior in one case but superior in other cases compared to those by pure mathematical optimization methods. For triple objective function optimization, the Pareto plots presenting multiple trade-off solutions are generated. In general, compared to high-pressure operation mode, the product recovery and power consumption for low-pressure operation mode are lower. For almost all the cases studied, CMC configuration with its high flexibility appears in the optimal solutions.     

Supporting the design process with hypergraph genetic operators

In this paper an evolutionary technique is proposed as a method for generating new design solutions with genotypes represented in the form of hierarchical hypergraphs. Such hypergraph-based evolutionary design requires the adaptation of traditional evolutionary operators like cross-over and mutation. This paper presents an attempt at defining modified cross-over and mutation operators that act on hierarchical hypergraphs. The application of the proposed transformations is illustrated on examples of designing floor layouts and furniture with the use of the evolutionary method.     

贵州省煤中有害微量元素的地球化学特性

对贵州省主要产煤区特别是西南部高污染无烟煤进行了系统采样,应用电感耦合等离子体原子吸收光谱(ICP-AES)、原子荧光光谱(AFS)、离子选择性电极和化学方法测定了煤中As,Hg,F,Cl,Cr,Cd,Mo等有害微量元素和形态硫的含量特性,应用等离子体低温灰化仪分离出煤中的无机矿物质,测试结果表明,煤中As,Hg,F,Cr,Cd,Mo等元素的含量均显著高于我国煤中的平均值,含量异常高的煤主要分布在贵州省的西南部.微量元素与矿物质总量之间的相关性并不十分明显,Hg与As与黄铁矿之间显著相关.     

基于遗传算法优化神经网络的房地产评估模型及实证研究

针对房地产价格和影响因素之间存在复杂且非线性的关系,用传统评估方法预测房地产价格精度不高的缺陷,提出一种基于遗传算法(Genetic Algorithm,GA)和BP神经网络(BP Neural Network,BPNN)的房地产评估预测模型。该模型利用BPNN确定房地产价格影响因子与评估价格之间的函数映射关系,并采用GA优化BPNN的权值和阈值来提高BPNN的收敛速度,解决了算法陷入局部极值点的问题。最后,通过对100组实际交易样本数据的计算机仿真实验,证明了该方法对房地产估价的有效性和准确性。     

基于改进遗传算法和SVM的森林火灾视频目标鉴别

计算机视觉是目前计算机技术研究的热门课题之一,目标对象特征的选择和提取是计算机视觉的核心问题。在分析和研究火焰图像中火焰预判方法的基础上,提出了从纹理、动态、几何3个方面筛取火焰图像的组合特征,并且重点介绍了动态特征的提取方法。在火焰识别的特征选择方面做了较深入的研究,综合利用了火焰的内容特征,基于遗传算法提出了新的适应度函数,使得特征选择更加科学,而基于结构风险最小化理论的SVM在识别过程中可充分发挥优势,因此在实验中取得了理想的效果。     

Evaluation of optimisation techniques and control variable formulations for a batch cooling crystallization process

The mathematical optimisation of a batch cooling crystallization process is considered in this work. The objective is to minimize the standard deviation of the final crystal size distribution (CSD), which is an important feature in many industrial processes. The results with the problem written as a nonlinear programming and solved with the successive quadratic programming (SQP) coupled with the discretization of the control variable are compared with those obtained when SQP coupled with the parameterisation of the control variable is applied. Also it is proposed the implementation of the genetic algorithm (GA) coupled with parameterisation of the control variable. Extensive evaluations show that the SQP method is sensitive both to the parameterisation formulation and to the initial estimate. The solution with GA provided the control variable profile that leads to the minimum standard deviation of the final CSD. Nevertheless, it is a very time-consuming technique, which hampers its utilization in real time applications. However, its feature of global searching suggests its suitability in solving offline problems, in order to provide initial setup profiles. Bearing this in mind, it is proposed an algorithm which allows for the implementation of GA solution in a real time fashion, taking advantage of its robustness to find out the optimal solution.