栉孔扇贝种群的遗传变异分析

采用不连续聚烯酰胺凝胶电泳技术研究了中国栉孔扇贝和日本栉孔扇贝两个天然种群的六种同工酶。结果表明,两个种群具有基本相同的基因位点控制同工酶的表达,二者都具有居群内的个体差异,中国和日本的两个种群的多态位点比例（P．99）分别为41．18％和45．45％,平均杂合度观测值（Ho)分别为0．1295和0．1531,平均杂合度预期值（He)分别为0．1695和0．2331,各位点平均等位基因数（Ne)分别为1．5294和1．6363。日本栉孔扇贝自然种群的遗传变异明显高于中国栉孔扇贝自然种群。同时各群体中普遍存在杂合子缺失现象。     

利用AFLP技术研究海湾扇贝不同养殖群体的遗传结构及其分化

采用AFIP分子标记技术对我国海湾扇贝的4个不同地理群体共80个个体利用7对引物组合进行了遗传多样性分析。4群体的多态位点比例分别为：加拿大养殖群体48．8235％,墨西哥湾养殖群体49．2914％,秦皇岛养殖群体38．2353％,浙江养殖群体41．1765％。平均杂和度分别为0．1878,0．1886,0．1265和0．1618。遗传距离介于0．1188～0．0941之间。4个群体的Fst为0．1883。根据遗传距离绘制了UPGMA聚类图。4个群体间的遗传关系如下：加拿大群体和浙江群体聚为一支,墨西哥湾群体和秦皇岛群体聚成一支,最后这两支聚在一起。试验结果表明,经过长时间累代养殖的浙江、加拿大、墨西哥湾群体仍保持较高的杂合度,与引种时间最短的(2年)、最为接近美国自然群体的秦皇岛群体比较,以上3个群体没有出现多态位点比例和杂合度显著下降的现象。     

栉孔扇贝不同地理群体的遗传多样性分析

应用AFLP技术对栉孔扇贝的中国长岛群体和韩国东部、西部群体进行了遗传多样性分析。实验表明，中国长岛群体的群体内遗传相似度最高，为0．6754，韩国东部群体次之，为0．6714，韩国西部群体最低，为0．6309；中国长岛群体与韩国东部、西部群体间的遗传距离分别为0．1703和0．1786，韩国两群体间的遗传距离只有0．057。这一结果说明，长岛群体与韩国群体遗传差异较大，韩国两个群体遗传相似度较高，应视为同一个群体。本研究共获得6个韩国两群体的共享特征标记，4个长岛群体的特异性标记，1个韩国西部群体所特有的标记，这些标记可以用于鉴定和区分这三个群体。为提高我国栉孔扇贝群体遗传多样性，以引进韩国西部群体为好。     

利用AFLP技术分析中国明对虾的韩国南海种群和养殖群体的遗传差异

利用AFLP技术对新发现的中国明对虾的一个地理种群——韩国南海种群（SP）和中国明对虾的养殖群体（CP）进行了遗传分析。每个群体随机取样30个,5对AFLP引物获得326个位点。其中SP多态位点比例（P0.99）46.93％,Shannon多样性指数（Ⅰ）0．1884,Nei（1978）基因多样性指数（h）0．1197,群体差异性位点9个,占检测位点总数的2．7％。CP多态位点比例（R9q）51．84％,Shannon多样性指数（Ⅰ）0．1954,Nei（1978）基因多样性指数（h）0．1229,群体差异性位点19个,占检测位点总数的5．8％。SP种群各项遗传参数都低干CP种群。两个群体的非偏差遗传相似度和遗传距离分别为0．9899和0．0102。利用中国明对虾的韩国南海种群和养殖群体杂交可以获得新的种质资源,这将为获得最大变异数量性状表型和基因多样性的产生提供可能。     

龙须菜(Gracilaria lemaneiformis)选育品系遗传背景的RAPD分析

在优化的RAPD反应条件下,筛选出30条随机引物,用于龙须菜野生型青岛一个群体和选育品系福建莆田、江苏连云港、山东荣成3个栽培群体的DNA多态性分析,共扩增出263条DNA片段。龙须菜野生型青岛群体和选育品系的山东荣成栽培种群的平均杂和度为0．160,0．120,多态位点比例为40．68％,32．32％;两群体问的遗传距离为0．1136,遗传相似性为0．8926。福建莆田、江苏连云港、山东荣成3个栽培群体的平均杂和度为0．141,0．123,0．120,多态位点比例为36．50％,33．08％,32．32％;山东荣成／福建莆田,山东荣成／江苏连云港,福建莆田／江苏连云港栽培群体间的遗传距离分别为0．1358,0．1425,0．0722,遗传相似率分别为0．8730,0．8672,0．9303。研究结果表明,龙须菜选育品系来源于野生型青岛群体,其快速生长的品系特征和遗传物质基础在不同海区环境保持相对稳定;龙须菜野生型青岛群体的遗传变异水平高于选育品系群体,选育品系的变异水平随着栽培海区由北方向南方呈现增加的趋势。     

泥蚶遗传多样性的研究

采用垂直聚丙烯酰胺同工酶电泳技术对我国及周边海域的7个泥蚶群体的11种同工酶、近28个位点的表型变异及遗传分化进行了研究,结果发现,绝大多数同工酶在7个群体之间谱型上都存在较大差异。遗传变异统计结果表明：7个泥蚶群体多态位点比例（P．99）和（P．95）分别在39．3％～53．6％和28．6％～44．4％之间;平均杂合度观测值在0．035～0．083之间,平均杂合度预期值在0．086～0．186之间;而平均位点等位基因数在1．786～2．381之间。比较了7个群体之问的遗传相似度和遗传距离表明,7个泥蚶群体明显地分为两大类群,第一个类群包括釜山、荣成、奉化、乐清、福鼎5个群体,它们的平均遗传距离是0．0072;第二个类群包括汕头和湛江两个群体,它们的遗传距离是0．0320;而这两个类群问的不同群体问的平均遗传距离为0．4279。因此这两个类群的分类学地位值得进一步探讨。     

栉孔扇贝EST中微卫星标记的筛选

使用RepeatMasker软件在栉孔扇贝的6935条ESTs中发现了42条微卫星序列。选取其中的7个序列，根据微卫星位点的旁侧序列设计引物，并对设计出的引物在中国、日本、韩国野生群体中进行多态性检测，发现有6对引物能产生扩增产物，其中有3对引物可望在以后的群体分析及连锁图谱的构建中加以应用。该结果初步证实了在栉孔扇贝的ESTs序列中存在微卫星位点，从而为扇贝生物中微卫星标记的筛选开辟了新的途径。     

中日栉孔扇贝杂交子一代群体的遗传变异

采用不连续聚丙烯酰胺凝胶电泳技术，研究了中国栉孔扇贝和日本栉孔扇贝的两组杂交子代群体的14个位点24个等位基因。将它们各自的自交子一代作为亲本的参照组，对这四组子代群体进行了遗传变异分析，结果表明：四组群体具有相同的基因位点控制同工酶的表达，但各位点等位基因的频率有所不同。统计结果表明：杂交群体的遗传变异明显高于自交群体的遗传变异，四组群体的多态位点比例（P.99）分别为36.36%（CP中自交组）、30.77%（JP日自交组）、30.77%（JFCM日雌中雄杂交组）、30.77%（JM-CF日雄中雌杂交组），平均杂合度观测值（Ho）分别为0.1273（CP）、0.1055（JP)、0.1536（JFCM）、0.1727（JMCF)，各位点平均有效等位基因数（Ae）分别为0.7318（CP）、1.6718（JP）、1.6955(JFCM)、1.6927（JMCF）。杂交优势已初步显示出来。     

栉孔扇贝×虾夷扇贝子一代杂种优势的RAPD分析

应用RAPD技术对栉孔扇贝×虾夷扇贝子一代的杂种优势进行了研究,对栉孔扇贝(C)、虾夷扇贝(P)及其正交F1(C♀×P♂)、反交F1(P♀×C♂)共4个群体的RAPD扩增带谱进行了分析。从40个随机引物中筛选出16个扩增带丰富的引物进行扩增,共得到l15条清晰稳定的扩增带,扩增片段大小在200～2000bp之间,其中有82个扩增位点具有多态性,多态性位点比率为71.3%。栉孔扇贝与正反交子代的相对遗传距离分别为0.0852和0.2886;虾夷扇贝与正反交子代的相对遗传距离分别为0.2327和0.0559,杂种子代与两亲本的遗传差异不是对等的,而是偏向各自的母本,系统树、Shannon多样性指数同样证明了这一点。两亲本群体内相似性指数分别为0.8392和0.8451,均大于正交子代的群体内相似性指数,而小于反交子代的群体内相似性指数,表明正交子代群体的遗传多样性水平升高,产生杂种优势;反交降低,未产生明显的杂种优势,与4个群体Shannon遗传多样性指数计算分析结果一致。     

扇贝种间单对杂交一代幼虫ISSR标记的分离方式

通过对栉孔扇贝♀和华贵栉孔扇贝舍单对培育的♂个全同胞F1幼虫进行ISSR标记,分析了双亲位点在杂交子代中的传递分离方式。所得到的总位点数中双亲和杂交子代共有位点1#家系占35．4％,2#家系占27．1％,两单亲与杂交一代共享位点将近占30％左右,表明双亲所携带的遗传物质皆传递给了F1代,证实种间杂交的成功。但杂交种F1从栉孔扇贝获得的位点数稍多于华贵栉孔扇贝,且前者的位点在F1中出现频率明显高于后者。另外F1的每个个体与栉孔扇贝的遗传距离皆小于与华贵栉孔扇贝的距离,说明杂交子代在遗传上明显地偏向母本栉孔扇贝。另外在杂交子代中出现了较高比例的非孟德尔分离位点和非亲位点。     

Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution

This article compares genetic algorithm (GA) and genetic programming (GP) for system modeling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analyzed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.     

Optimization of Cellular Automata Model for the Heating of Dual-Phase Steel by Genetic Algorithm and Genetic Programming

This study considers a common metallurgical problem associated with the phase transformation of steel during heating where austenite grain tends to grow in size with time and results in poor mechanical properties in the final stages. This investigation was performed using a Cellular Automata model for dual-phase steel developed in house. Data-driven metamodels for a biobjective optimization problem involving minimizing average austenite grain size along with the maximizing of time of heating were constructed using Evolutionary Neural Network (EvoNN) and Biobjective Genetic Programming (BioGP). The input variables selected for this task were (i) heating rate, (ii) pearlite percentage, (iii) nucleation density of austenite, and (iv) the finish temperature of austenite formation. The analyses of the results led to the fact that heating rate is the most influencing factor and it needs to be large during transformation to obtain a refined microstructure. The comparison of Pareto front between EvoNN and BioGP reveals a better performance of the latter. Limited experimental confirmation was also carried out.     

Integrated Genetic Programming and Genetic Algorithm Approach to Predict Surface Roughness

In this article we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimized by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modeling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.     

基因遗传优化算法研究及应用探索

在探讨基因遗传算法机理的基础上，提出了一种基于同源杂交和基因逐位收敛概念的算法。对一典型算例作了参数研究，并作了应用于结构振动模态参数非线性识别等的尝试。讨论了这一算法进一步发展的几个方面．     

Process Control and Optimization of the AOD Process Using Genetic Algorithm

The AOD process of making stainless steel is very popular because of its fast rate of decarburization and excellent control of composition and temperature. Both static and dynamic models are available for process control. The objective of the present study is to demonstrate the applicability of GA in adapting a process control model to a particular plant environment and subsequently optimize the blowing scheme to reduce overall costs. Deviation of the predicted results of the process control model, from the actual plant data, is first minimized by adjusting the chemical reaction rate parameters and heat balance equation. It is found that the adaptation of the process control model to an actual situation is feasible only below 0.8% carbon. After adapting the model, optimization is done through GA wherein the objective function contains the cost of refractory, process time, chromium loss etc. It is thus possible to devise a blowing scheme to arrive at the desired end point composition and temperature at the lowest cost.     

基于时间满意的覆盖问题及混合遗传算法实现

利用时间满意度函数,从顾客角度考虑覆盖半径,从企业角度考虑覆盖比例,提出了比传统集覆盖问题更一般的基于时间满意的覆盖选址问题.通过建立这一问题的整数规划模型,应用混合遗传算法对该问题求解,并同几种被证明为在覆盖选址问题中计算效果较好的启发式算法进行了对比实验分析,实验显示本文的算法策略可以得到高质量的解.     

Genetic optimization of stiffened plates and shells

This work gives two examples of application of stochastic techniques for the optimization of stiffened plates or shells. The research strategy consists in substituting, for finite‐element calculations in the optimization process, an approximate response of a neural network, or an approximate response from the Ritz method. More precisely, the paper describes the use of a backpropagation neural network or the Ritz method in creating function approximations for use in computationally intensive design optimization based on genetic algorithms. Two examples of applications are presented; the first one deals with the optimization of stiffeners on a plate by varying their positions, while having well‐defined dimensions; the second example deals with the optimization of a thin shell subject to buckling. Copyright © 2001 John Wiley & Sons, Ltd.