基于混合遗传算法的树状注水管网拓扑优化

以投资最小为目标函数,以井间隶属关系的惟一性、连接注水井数量等限制为约束条件,建立了树状注水管网拓扑优化数学模型.根据模型特点,将优化问题分为分配层和几何层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,采用随机多父辈适应函数值加权交叉和多种变异操作,结合了模拟退火算法,并给出了初温的确定方法,改进了选择复制操作,从而形成了混合遗传算法.该算法能够有效地提高收敛速度,避免早熟收敛.利用算例说明了该方法的有效性.     

基于模糊度测度和改进遗传算法的图像分割

为了利用模糊集理论进行图像分割,本文提出了一种模糊度测度函数,它衡量了背景模糊集和某一个二值图像的相似性:模糊度越小,这种相似程度越大;并且对模糊度的计算只需要进行简单的比较运算和加法运算,便于硬件实现。本文利用遗传算法求取最佳阈值,针对遗传算法的某些不足之处.我们利用精英策略和模拟退火的思想,对其做了一些修正。实验结果表明:相对于Shannon熵方法和模糊熵方法,本文方法取得了较好的分割结果,并且具有较强的抗噪声能力。     

Design optimization of 3D steel structures: Genetic algorithms vs. classical techniques

In this paper an elitist genetic algorithm (GA) developed by the authors is compared with common commercial solutions for complex structural optimization. After its prior validation on two-dimensional (2D) structures, the GA was tuned and improved in order to obtain structural elements with minimum weights that satisfy the ultimate limit states of the applicable building code. Subsequently, the same spatial structures were optimized using a commercial structural analysis program. Finally, the cost and weight improvements obtained using the GA, although at a higher computational cost, are discussed.     

基于Petri网的供需链设计优化研究

供需链设计优化问题由于其自身的复杂性而成为一个极具挑战性的研究课题。本文把Petri网与遗传算法结合起来，用于研究供需链设计优化，构建一个供需链原型测试系统，并对小汽车制造供需链系统进行了实例分析。     

油田注水地面系统拓扑布局优化新方法

介绍了油田地面注水系统拓扑布局优化的数学模型及计算方法,举例计算表明,用遗传算法解决油田注水布局能取得良好的结果。     

小生境遗传算法AVOA反演

采用具有水平对称轴的横向各向同性(HTI)模型研究裂缝性油气藏,P波振幅随炮检距和方位角变化(AVOA),表现出较强的方位各向异性特征。本文基于Ruger提出的HTI介质模型的P波反射系数公式,利用由基准测线和另外两条与之呈45°、90°测线的P波反射系数的相对差异估算裂缝方位;同时引入小生境遗传算法,对基准测线和与之呈90°测线的P波反射系数差进行反演,得到了较高精度的纵、横波波速比和Thomsen各向异性参数。     

A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation

A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach.     

Genetic algorithms for symbolic clustering

This paper introduces a novel methodology for clustering of symbolic objects by making use of Genetic Algorithms (GAs). GAs are a family of computational models inspired by evolution. These algorithms encode potential solutions to specific problems on simple chromosome-like data structures and apply recombination operators to these structures so as to preserve critical information. A new type of representation for chromosome structure is presented here along with a new method for mutation. The efficacy of the proposed method is examined by application to numeric data of known number of classes and also to assertion type of symbolic objects drawn from the domain of fat oil, microcomputers, microprocessors and botany. The validity of the clusters obtained is examined.     

Genetic algorithms for optimal logical database design

The focus of this paper is database design using automated database design tools or more general CASE tools. We present a genetic algorithm for the optimization of (internal) database structures, using a multi-criterion objective function. This function expresses conflicting objectives, reflecting the well-known time/space trade-off. This paper shows how the solution space of the algorithm can be set up in the form of tree structures (forests), and how these are encoded by a simple integer assignation. Genetic operators (database transformations) defined in terms of this encoding behave as if they manipulate tree structures. Some basic experimental results produced by a research prototype are presented.     

Mapping of the Saccharomyces cerevisiae CDC3, CDC25, and CDC42 genes to chromosome XII by chromosome blotting and tetrad analysis

CDC3, CDC25 and CDC42 were localized to chromosome XII by hybridizing the cloned genes to Southern blots of chromosomes separated by orthogonal-field-alternation gel electrophoresis. Meiotic tetrad analyses further localized these genes to the region distal to the RDN1 locus on the right arm of the chromosome. The STE11 gene, which had previously been mapped to chromosome XII (Chaleff and Tatchell, 1985), was found to be tightly linked to ILV5. The data suggest a map order of CEN12-RDN1-CDC42-(CDC25-CDC3)-(ILV5- STE11)-URA4. Certain oddities of the data set raise the possibility that there may be constraints on the patterns of recombination in this region of chromosome XII.