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.     

高坝流激振动响应的反分析方法

为分析高速水流诱发高坝泄流结构的振动问题，根据结构动力学、随机振动理论和最优方法的基本理论，提出了基于谱分析及遗传算法的振源反分析方法，采用本文方法，可以由少量测点的动位移实测值反馈分析出各激振源等效荷载谱，通过流激振动响应的正分析方法得到整个结构在水流动力荷载作用下的动力响应，对水工结构的流激振动进行正确全面的评估，为结构损伤动态监测和诊断提供新的技术手段。并通过拉西瓦拱坝泄洪水弹性模型试验实测值与反分析计算值的比较验证了本文方法的合理性。     

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.     

Neural Networks Analysis of Steel Plate Processing Augmented by Multi‐objective Genetic Algorithms

An earlier neural network analysis of processing of steel plates through hot rolling was subjected to a further refined analysis through some flexible neural networks that evolved using a multi‐objective predator‐prey genetic algorithm. The original data set expressing the Yield Strength and Ultimate Tensile Strength of the rolled slabs in terms of a total of 108 process variables were subjected to a systematic pruning through this evolutionary approach, till the nitrogen content of the steel emerged as the most significant input variable. A theoretical explanation is provided for this slightly unexpected result.     

MALTING AND BREWING SCIENCE: CHALLENGES AND OPPORTUNITIES*,†

Molecular technologies have been developed for the transformation of barley. These technologies complement current methods of barley breeding. In addition, they offer the potential of altering specific components in barley that affect malting quality and of introducing foreign genes, controlling desirable traits, into barley. Application of genetic engineering to improving malt quality factors such as cell wall degradation, protein modification, starch hydrolysis and flavour stability, is discussed. Limitations to the use of this technology for improving malt-related functional properties of barley components such as cell walls and starch granules are also evaluated. Some possible constraints to the utilization of genetic engineering for malt quality improvements are identified.     

基于Matlab的线性规划问题求解

线性规划是运筹学中研究较早、发展较快、应用广泛、方法成熟的一个重要分支,它是辅助人们进行科学管理的一种重要的数学方法.文章首先介绍了线性规划的基本概念及标准形式,着重讨论了线性规划问题的三种常用解法:单纯形法、直接搜索法以及遗传算法,最后在Matlab R2009a环境下进行了仿真.通过结果可以看出,用Matlab求解线性规划问题,可以避免手工的烦琐计算,大大地提高工作效率和结果的准确性.     

遗传算法和Dijkstra算法在动态权值系统中的比较

针对遗传算法和Dijkstra算法在求解动态权值系统中最短路径时的性能问题,采用比较法,将两种算法应用在同一个实际游戏模型中,对其算法的稳定性、智能性、时间复杂度进行对比测试。游戏模型模拟了各种条件下的动态权值系统。为了使遗传算法更加可靠,通过优化其变异过程使得收敛速度更快,可靠性更高。实验数据表明,遗传算法在每张地图上的得分数以及算法所用时间普遍高于Dijkstra算法,从而得出遗传算法在求解动态权值系统中最短路径问题时稳定性和预期效果明显好于Dijkstra算法,但其时间复杂度较高的结论。     

自适应激素调节遗传算法

基于生物内分泌系统的激素调节原理,提出了一种新的自适应遗传算法。该算法以内分泌激素调节的Hill函数下降形式为基础,设计了自适应交叉算子和自适应变异算子,使交叉率和变异率在遗传算法迭代过程中,能够根据各代种群多样性的变化进行自适应调节,在整个进化过程中将种群多样性维持在合理水平。4种测试函数及三维人脑图像分割的实验结果显示,提出的自适应遗传算法可较好地保持种群多样性并克服早熟现象,性能优于其他两种自适应遗传算法及传统遗传算法。     

基于改进遗传算法的QoS感知Web服务组合

传统遗传算法在种群初始化的时候,普遍采用均匀取种法或随机取种法,这些方法生成的种群的平均适应度比较低,难以保证算法的搜索效率。文中提出一种改进的遗传算法用于QoS敏感的Web服务组合,采用两种不同的算法进行服务选择,避免了随机生成初始种群给算法带来的负面影响。并且,该算法将路径模板化以减少服务组合的工作量,用染色体可变长的编码方式来解决组合服务的多路径选择问题。通过仿真实验,与传统的算法相比,所提出的算法在实现服务组合时收敛更快,最优解的适应度更高。