Heuristic methods for evolutionary computation techniques

Evolutionary computation techniques, which are based on a powerful principle of evolution—survival of the fittest, constitute an interesting category of heuristic search. In other words, evolutionary techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival.Any evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; some of these heuristic rules provide guidelines for evaluating (feasible and infeasible) individuals in the population. This paper surveys such heuristics and discusses their merits and drawbacks.An abridged version of this paper appears in the volume entitled META-HEURISTICS: Theory & Application, edited by Ibrahim H. Osman and James P. Kelly, to be published by Kluwer Academic Publishers in March 1996.     

条形平板中裂纹识别的遗传算法

研究条形平板中平行于上下边界的单一裂纹的识别问题。给出了当一声波入射这一条形平板时，裂纹参数与反射、透射系数之间的关系式，并应用遗传算法对裂纹进行了定量识别。计算结果表明本文给出的方法具有较好的识别精度。     

Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing

Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science, technology, medicine, communication, etc. The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator, which performs a partial local search for randomly selected representatives of the population. This operator is aimed as a minor deterministic contribution to the (stochastic) genetic search. The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates (so called asymmetric mutation). The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide, varying such parameters as mutation rates and the learning rate. It is shown through the averaged (over the subpopulation) fitness behaviour, how the genetic diversity in the population depends on the mutation rate of the given subpopulation. Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example. Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator. The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level. The optimal values of these two parameters are about 6 and 5%, respectively. The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and, therefore, it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity.     

Structure‐based ligand design by a build‐up approach and genetic algorithm search in conformational space

Program to engineer peptides (PEP) is a build‐up approach for ligand docking and design with implicit solvation. It requires the knowledge of a seed from which it iteratively grows polymeric ligands consisting of any type of amino acid, i.e., natural and/or nonnatural from a user‐defined library. At every growing step, a genetic algorithm is used for conformational optimization of the last added monomer in the rigid binding site. Pruning is performed at every growing step by selecting sequences according to binding energy with electrostatic solvation. PEP is applied to three members of the caspase family of cysteine proteases using Asp at P₁ as seed. The optimal P₄–P₂ peptide recognition motifs and variants thereof are docked correctly in the active site (backbone root‐mean‐square deviation < 0.9 Å). Moreover, for each caspase, the P₄–P₂ sequences of potent aldehyde inhibitors are ranked among the 15 hits with the most favorable PEP energy. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1956–1970, 2001     

大花无柱兰遗传多样性的SRAP标记分析

大花无柱兰是一种珍稀兰科植物,具有一定的观赏和药用价值,但数量十分稀少,该物种亟待保护。本研究采用SRAP分子标记技术,对10个居群的115份DNA样品进行PCR扩增,并开展遗传多样性分析。从81对引物中筛选出9个条带清晰、多态性好、重复性高的引物组合,共扩增得到305条谱带。在物种水平上,多态性比率（PPB）为100%,Nei’s基因多样性指数（H）为0.209 8,Shannon’s指数（I）为0.340 2;在居群水平上,PPB为24.59%～52.13%,H为0.079 6～0.165 5,I为0.120 9～0.252 3。居群水平上,基因分化度（Gst）为0.520 9,基因流（Nm）为0.459 9,遗传距离为0.091 9～0.198 4。UPGMA聚类结果表明,10个居群可分为3大类,地理距离相近的居群优先聚集。大花无柱兰的遗传多样性较为丰富,居群间存在一定的遗传分化和基因交流,可采用就地保护和人工栽培等方式加以保护。     

基于均匀设计抽样遗传算法求解背包问题

众所周知,遗传算法的运行机理及特点是具有定向制导的随机搜索技术,其定向制导的原则是:导向以高适应度模式为祖先的"家族"方向.以此结论为基础,利用均匀设计抽样的理论和方法,对遗传算法中的交叉操作进行了重新设计,给出了一个新的GA算法,称之为均匀设计抽样遗传算法.最后将均匀设计抽样遗传算法应用于求解背包问题,并与简单遗传算...     

一种基于遗传算法的肽/蛋白质结合模式虚拟筛选建模技术

“合理”QSAR模型是指在了解配体与受体相互作用模式的前提下建立定量构效关系, 这样避开了传统做法仅仅依靠样本集分子自身信息来构建预测模型的诸多弊端. 本文将此思想应用于肽/蛋白质亲和活性的研究当中, 借助于遗传算法作为虚拟受体结合靶点及相互作用模式的筛选手段得到了一种新的建模技术: 肽/蛋白质结合模式遗传虚拟筛选(genetic virtual screening of combinative mode for peptide/protein, GVSC). 该法成功解决了“合理”QSAR研究中的难题, 即大多数情况下受体结构未知而难以了解配基与之发生的结合方式. 分别使用58个血管紧张素转化酶, 18个Camel抗体蛋白cAb-lys3双位点突变残基对GVSC加以检验, 其结果表明GVSC能够较好地阐明配基与受体之间的作用机理, 并能得到优于传统方法的QSAR模型.     

大麦抽穗期的遗传分析

本文对8×8大麦品种双列杂交的F_1和F_2的抽穗期进行了遗传分析,结果:(1)抽穗期性状在F_1双列试验中,W_r/V_r回归系数b(0.8399)与单位斜率1无显著差异,表明符合加性-显性遗传模型,在F_2中,b=0.2768,显著不同于1,存在上位效应。去阵列8后,对F_2双列资料重新检验,发现由此获得的亚双列系统符合模型(b=0.8230).W_r/V_r分析表明,F_1与F_2世代间阵列位置有变动,但阵列2,3在两个世代中均据回归线的上部.W_r+V_r与Y_r间的相关测定均为负相关,表明抽穗期晚一般由显性基因效应控制.(2)抽穗期F_1和F_2的广义遗传力为97％与98％,狭义遗传力为87％与69％.在F_1为0.59,表明抽穗期晚为部分显性.F_2却大于1,示有超显性特点;H_2/4H_1值都小于0.25,说明全部有关座位上,平均地说,显隐性等位基因频率不相等.F_1值为负值,表明所有亲本所携带的隐性基因比显性基因要多. (3)抽穗期一般配合力效应为负向的有小将,早熟3号和永2830.特殊配合力存在较大负向优势的杂交组合中,必含小将、早熟三号和永2830三个早熟品种之一为杂交亲本.     

星载张力索网天线结构混合变量的多目标优化

以纵向拉索调整力大小和可调整的纵向拉索数目为设计变量,可调整的纵向拉索数目最少和网状反射面的均方根误差最小为目标函数,应力和频率为约束条件,建立了大型空间网状可展开天线的形面调整的多目标优化数学模型,并用遗传算法进行了优化求解。优化结果表明:该方法在提高天线表面精度的同时,能有效地减少可调整的纵向拉索数目,降低了工程人员在网面调整中的繁杂劳动。并且,网面调整的总次数也远少于实际工程中的调整,对工程应用具有一定的指导意义。     

Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.