一种新的改进遗传算法及其性能分析外文翻译、中英文翻译、外文文献翻译

一种改进的混合遗传算法［提要］为了提高遗传算法的搜索效率，给出了一种改进的遗传算法。

该算法提出了新的交叉操作和仿粒子群变异，扩大了搜索范围。

通过经典函数的测试表明，改进算法与一般自适应遗传算法相比较，在函数最优值、平均收敛代数、收敛概率等方面都取得了令人满意的效果。

关键词：自适应遗传算法；适应度函数；交叉操作；仿粒子群变异中图分类号：TP3 文献标识码：A收录日期：2012年1月12日一、引言遗传算法（GA）由美国Michigan大学的Holland教授于1975年首先提出，后经De Jong、GoldBerg等人改进推广，广泛应用于各类问题。

它是一种模拟自然界生物进化过程与机制的全局概率优化搜索方法。

早期遗传算法在进化过程中易出现早熟收敛和局部收敛性差等问题，为了克服上述问题，人们提出了多种改进算法，本文针对遗传算法的不足，采用实数编码对遗传算法中的交叉和变异操作进行改进，提高了算法全局搜索能力，最后使用改进的算法进行仿真实验，结果表明本算法具有收敛概率高和平均收敛代数少的优点。

二、改进的遗传算法1、改进的交叉操作。

本文遗传算法采用实数编码，改进的交叉操作是先在交叉前产生三个服从均匀分布的随机数a∈［0，1］、b∈［－1，1］、c∈［－1，1］，然后假设x1，x2是要交叉的两个父代，个体变量为m维，则x1，x2可以表示为x1=（x11，x12，…，x1m），x2=（x21，x22，…，x2m），其中为位移变量，其中?驻ij=min｛（xij-），（－xij）｝（i=1，2；j＝1，2，…m），最后进行两次操作得：x1’=x1+b•?驻1x2’=x2+c•?驻2 （1）x1’’=ax1’+（1－a）x2’x2’’=ax2’+（1-a）x1’ （2）x1’，x2’，x1’’，x2’’分别为两次操作所产生的子代，从这4个子代中选取适应度大的两个保留到下一代。

通过这种操作可以有效地避免两个数值相近的个体进行“近亲繁殖”（数值相近的个体若只进行（2）式的操作会导致种群多样性快速下降），同时由于b，c的选取，生成的x1’，x2’是两个不相干的个体，彼此之间独立，由（2）式决定的后代还可以使子代遗传父代的某些有用因素，同时由于（1）式的位移调整，使得（2）式生成的后代比一般的算数交叉产生后代的范围扩大，提高算法的搜索范围，避免搜索陷入一个局部区域而出现“早熟”，最后再引入竞争机制在这四个后代中选出两个最好后代个体，这样在保证多样性的同时可以加快收敛的速度。

用于求解TSP问题的遗传算法改进【摘要】The traveling salesman problem (TSP) is a well-known combinatorial optimization problem that has been widely studied in the field of computer science. In this paper, we focus on using genetic algorithms to solve the TSP problem and explore various strategies to improve the performance of genetic algorithms in this context.【关键词】TSP问题、遗传算法、改进策略、实验设计、效果评估、实际应用、未来研究、背景介绍、研究意义、研究目的、遗传算法原理、遗传算法在TSP问题中的应用、改进方法实验设计、评估效果、展望未来1. 引言1.1 背景介绍Traditional methods for solving the TSP involve exhaustive search algorithms, such as the brute-force approach or dynamic programming. However, these methods become computationally expensive as the number of cities increases,making them impractical for real-world problems with large datasets.1.2 研究意义TSP问题（Traveling Salesman Problem）是一种经典的组合优化问题，用于描述一个旅行商从一个城市出发，经过每个城市且仅经过一次，最终回到出发城市的路径规划问题。

中英文对照外文翻译文献(文档含英文原文和中文翻译)译文:GA算法优化IIR滤波器的设计摘要本文提出了运用遗传算法(GA)来优化无限脉冲响应数字滤波器(IIR)的设计。

IIR滤波器本质上是一个递归响应的数字滤波器。

由于IIR 数字滤波器的表面误差通常是非线性的和多峰的，而全局优化技术需要避免局部最小值。

本文提出了启发式方式来设计IIR滤波器。

GA是组合优化问题中一种功能强大的全局优化算法，该论文发现IIR数字滤波器的最佳系数可以通过GA 优化。

该设计提出低通和高通IIR数字滤波器的设计，以提供过渡频带的估计值。

结果发现，所计算出的值比可用于过滤器的在MATLAB设计FDA工具更优化。

举个例子，采用的仿真结果表明在过渡带和均方误差(MSE)的改善。

零极点的位置也被提出来用来描述系统的的稳定性，以便将结果与模拟退火(SA)的方法相比较。

关键词：数字滤波器;无限冲激响应(IIR);遗传算法(GA);优化1.说明在过去的几十年中的数字信号处理(DSP)领域已经成长太重要的理论和技术。

在DSP中，有两个重要的类型系统。

第一类型的系统是执行信号滤波的时域，因此它被称为数字滤波器。

第二类型的系统提供的信号表示频域，被称为频谱分析仪。

数字滤波是DSP的最有力的工具之一。

数字滤波器能够性能规格，最好的同时也是极其困难的，而且不可能的是，先用模拟滤波器实现。

另外，数字滤波器的特性，可以很容易地在软件控制下发生变化。

数字滤波器被分类为有限持续时间脉冲响应(FIR)滤波器或无限持续时间脉冲响应(IIR)滤波器，这取决于该系统的脉冲响应的形式。

在FIR系统中，脉冲响应序列是有限的持续时间，即，它具有非零项的数量有限。

数字无限脉冲响应(IIR)滤波器通常可以提供比其等效有限脉冲响应(FIR)滤波器更好的性能和更少的计算成本，并已成为越来越感兴趣的目标。

但是，由于IIR滤波器的误差表面通常是非线性的，多式联运，传统的基于梯度的设计方法可以很容易地陷入错误的表面。

遗传算法中英文对照外文翻译文献(文档含英文原文和中文翻译)Improved Genetic Algorithm and Its Performance Analysis Abstract: Although genetic algorithm has become very famous with its global searching, parallel computing, better robustness, and not needing differential information during evolution. However, it also has some demerits, such as slow convergence speed. In this paper, based on several general theorems, an improved genetic algorithm using variant chromosome length and probability of crossover and mutation is proposed, and its main idea is as follows : at the beginning of evolution, our solution with shorter length chromosome and higher probability of crossover and mutation; and at the vicinity of global optimum, with longer length chromosome and lower probability of crossover and mutation. Finally, testing with some critical functions shows that our solution can improve the convergence speed of genetic algorithm significantly , its comprehensive performance is better than that of the genetic algorithm which only reserves the best individual.Genetic algorithm is an adaptive searching technique based on a selection and reproduction mechanism found in the natural evolution process, and it was pioneered by Holland in the 1970s. It has become very famous with its global searching,parallel computing, better robustness, and not needing differential information during evolution. However, it also has some demerits, such as poor local searching, premature converging, as well as slow convergence speed. In recent years, these problems have been studied.In this paper, an improved genetic algorithm with variant chromosome length and variant probability is proposed. Testing with some critical functions shows that it can improve the convergence speed significantly, and its comprehensive performance is better than that of the genetic algorithm which only reserves the best individual.In section 1, our new approach is proposed. Through optimization examples, in section 2, the efficiency of our algorithm is compared with the genetic algorithm which only reserves the best individual. And section 3 gives out the conclusions. Finally, some proofs of relative theorems are collected and presented in appendix.1 Description of the algorithm1.1 Some theoremsBefore proposing our approach, we give out some general theorems (see appendix) as follows: Let us assume there is just one variable (multivariable can be divided into many sections, one section for one variable) x ∈ [ a, b ] , x ∈ R, and chromosome length with binary encoding is 1.Theorem 1 Minimal resolution of chromosome is s =12l --a b Theorem 2 Weight value of the ith bit of chromosome isw i =12l --a b 12-i ( i = 1,2,…l ) Theorem 3 Mathematical expectation Ec(x) of chromosome searching step with one-point crossover isE c (x) = la b 2-P c where Pc is the probability of crossover.Theorem 4 Mathematical expectation Em ( x ) of chromosome searching step with bit mutation isE m ( x ) = ( b- a) P m1. 2 Mechanism of algorithmDuring evolutionary process, we presume that value domains of variable are fixed, and the probability of crossover is a constant, so from Theorem 1 and 3, we know that the longer chromosome length is, the smaller searching step of chromosome, and the higher resolution; and vice versa. Meanwhile, crossover probability is in direct proportion to searching step. From Theorem 4, changing the length of chromosome does not affect searching step of mutation, while mutation probability is also in direct proportion to searching step.At the beginning of evolution, shorter length chromosome( can be too shorter, otherwise it is harmful to population diversity ) and higher probability of crossover and mutation increases searching step, which can carry out greater domain searching, and avoid falling into local optimum. While at the vicinity of global optimum, longer length chromosome and lower probability of crossover and mutation will decrease searching step, and longer length chromosome also improves resolution of mutation, which avoid wandering near the global optimum, and speeds up algorithm converging.Finally, it should be pointed out that chromosome length changing keeps individual fitness unchanged, hence it does not affect select ion ( with roulette wheel selection) .1. 3 Description of the algorithmOwing to basic genetic algorithm not converging on the global optimum, while the genetic algorithm which reserves the best individual at current generation can, our approach adopts this policy. During evolutionary process, we track cumulative average of individual average fitness up to current generation. It is written as X(t) = G 1∑=G t avg f1(t)where G is the current evolutionary generation,avg f is individual averagefitness. When the cumulative average fitness increases to k times ( k> 1, k ∈ R) of initial individual average fitness, we change chromosome length to m times ( m is a positive integer ) of itself , and reduce probability of crossover and mutation, whichcan improve individual resolution and reduce searching step, and speed up algorithm converging. The procedure is as follows:Step 1 Initialize population, and calculate individual average fitness0avg f ,and set change parameter flag. Flag equal to 1.Step 2 Based on reserving the best individual of current generation, carry out selection, regeneration, crossover and mutation, and calculate cumulative average of individual average fitness up to current generationavg f ;Step 3 If 0avg avgf f ≥k and Flag equals 1, increase chromosome length to m times of itself, and reduce probability of crossover and mutation, and set Flag equal to 0; otherwise continue evolving.Step 4 If end condition is satisfied, stop; otherwise go to Step 2.2 Test and analysisWe adopt the following two critical functions to test our approach, and compare it with the genetic algorithm which only reserves the best individual: ()]01.01[5.0sin 5.0),(2222221y x y x y x f ++-+-= ]5,5[ ∈,-y x))4cos(4.0)3cos(3.02(4),(222y x y x y x f ππ--+-= ]1,1[ ∈,-y x2. 1 Analysis of convergenceDuring function testing, we carry out the following policies: roulette wheel select ion, one point crossover, bit mutation, and the size of population is 60, l is chromosome length, Pc and Pm are the probability of crossover and mutation respectively. And we randomly select four genetic algorithms reserving best individual with various fixed chromosome length and probability of crossover and mutation to compare with our approach. Tab. 1 gives the average converging generation in 100 tests.In our approach, we adopt initial parameter l0= 10, Pc0= 0.3, Pm0= 0.1 and k=1.2, when changing parameter condition is satisfied, we adjust parameters to l= 30, Pc= 0.1, Pm= 0.01.From Tab. 1, we know that our approach improves convergence speed of genetic algorithm significantly and it accords with above analysis.2. 2 Analysis of online and offline performanceQuantitative evaluation methods of genetic algorithm are proposed by Dejong, including online and offline performance. The former tests dynamic performance; and the latter evaluates convergence performance. To better analyze online and offline performance of testing function, w e multiply fitness of each individual by 10, and we give a curve of 4 000 and 1 000 generations for f1 and f2, respectively.(a) online (b) onlineFig. 1 Online and offline performance of f1(a) online (b) onlineFig. 2 Online and offline performance of f2From Fig. 1 and Fig. 2, we know that online performance of our approach is just little worse than that of the fourth case, but it is much better than that of the second, third and fifth case, whose online performances are nearly the same. At the same time, offline performance of our approach is better than that of other four cases.3 ConclusionIn this paper, based on some general theorems, an improved genetic algorithmusing variant chromosome length and probability of crossover and mutation is proposed. Testing with some critical functions shows that it can improve convergence speed of genetic algorithm significantly, and its comprehensive performance is better than that of the genetic algorithm which only reserves the best individual.AppendixWith the supposed conditions of section 1, we know that the validation of Theorem 1 and Theorem 2 are obvious.Theorem 3 Mathematical expectation Ec(x) of chromosome searching step with one point crossover is Ec(x) = c P l a b 2-where Pc is the probability of crossover.Proof As shown in Fig. A1, we assume that crossover happens on the kth locus, i. e. parent’s locus from k to l do not change, and genes on the locus from 1 to k are exchanged.During crossover, change probability of genes on the locus from 1 to k is 21(“1” to “0” or “0” to “1”). So, after crossover, mathematical expectation of chromosome searching step on locus from 1 to k is)12(12212122121)(111-•--•=•--•==-==∑∑k l j k j l j kj ck a b a b w x E Furthermore, probability of taking place crossover on each locus of chromosome is equal, namely l 1Pc. Therefore, after crossover, mathematical expectation of chromosome searching step is)(1)(11x E P lx E ck c l k c ••=∑-= Substituting Eq. ( A1) into Eq. ( A2) , we obtain )1211(2)(])12[(122)12(12211)(11---•=--•--•=-•--•••=∑-=l c i l c k l c l k c l a b P l a b l P a b P lx E where l is large, 012≈-l l , so )(x E c c P l a b 2-≈Fig. A1 One point crossoverTheorem 4 Mathematical expectation)(x E m of chromosome searching step with bit mutation m m P a b x E •-=)()(, where Pm is the probability of mutation.Proof Mutation probability of genes on each locus of chromosome is equal, say Pm, therefore, mathematical expectation of mutation searching step isE m (x )=P m ·w i =i =1l åP m ·b -a 2l -1·2i -1=i =1l åP m ·b -a 2i -1·(2i -1)=(b -a )·P m一种新的改进遗传算法及其性能分析摘要：虽然遗传算法以其全局搜索、并行计算、更好的健壮性以及在进化过程中不需要求导而著称，但是它仍然有一定的缺陷，比如收敛速度慢。

生物医学专业基因编辑外文文献翻译、中英文翻译、外文翻译摘要本文翻译了生物医学专业的基因编辑外文文献，包括中英文翻译和外文翻译。

详细内容见下文。

中英文翻译1. 标题：生物医学专业基因编辑（Biomedical Gene Editing）基因编辑是一种针对生物体遗传信息进行改变的技术。

通过编辑基因组，可以修改或插入DNA序列，从而改变目标生物体的遗传特征。

基因编辑在生物医学领域具有广泛的应用前景。

2. 摘要（Abstract）基因编辑已成为生物医学研究中的重要工具。

它不仅可以用于疾病诊断和治疗，还可以用于基础科学研究和生物技术开发。

随着技术的不断发展，基因编辑对人类健康和生物多样性的影响也引起了人们的关注。

3. 引言（Introduction）基因编辑是一种通过改变生物体的遗传信息来实现特定目的的技术。

它可以在DNA水平上对基因组进行精确的修改和操控，从而实现对目标生物体特征的变化。

外文翻译1. Title: Biomedical Gene EditingGene editing is a technology that allows for changes to be made to the genetic information of an organism. By editing the genome, DNA sequences can be modified or inserted, altering the genetic characteristics of the target organism. Gene editing has wide applications in the field of biomedical research.2. Abstract3. IntroductionGene editing is a technology that achieves specific objectives by altering the genetic information of an organism. It allows for precise modifications and manipulation of the genome at the DNA level, resulting in changes to the characteristics of the target organism.。

外文科技资料翻译英文原文Research on a face recognition system by the genetic algorithmComputer vision and recognition is playing anincreasingly important role in modern intelligent control.Object detection is the first and most important step inobjectrecognition.Traditionally,a special object can berecognized by the template matching method,but the recognition speed has always been a problem.In thisarticle,animproved general genetic algorithm-based face recognitionsystem is proposed.The genetic algorithm(GA)has beenconsidered to be a robust and global searching method.Here,the chromosomes generated by GA contain the information needed to recognize the object.The purpose of thisarticle is to propose a practical method of face detection andrecognition.Finally,the experimental results,and a comparison with the traditional template matching method,andsome otherconsiderations,are also given.1 IntroductionIf we search on the web or in a conference proceedingsabout intelligent control,lots of papers and applications arepresented.Among them,image processing and recognitionoccupy a very large percentage.The higher the degree ofintelligence,the more important the image detection andrecognition technology.For controlling an intelligent system(autonomous mobile vehicle,robot,etc.),the most important element is thecontrol strategy,but before automatically making it move,image recognition is needed.For an intelligent control system,it is necessary to acquire information about the external world automatically by sensors,in order to recognize itsposition and the surrounding situation.A camera is one ofthe most important sensors for computer vision.That is tosay,the system endeavors to find out what is in an image(the environment of the robot)taken by the camera:trafficsigns,obstacles,guidelines,etc.The reliability and time-response of object detection andrecognition have a major influence on the performance andusability of the whole object recognitionsystem.1Thetemplatematching method is a practicable and reasonablemethod for object detection.2This article gives an improvement in the general template matching method.In addition,in order to search for the object of interestin an image,lots of data need to be processed.The geneticalgorithm(GA)has been considered to be a robust andglobal searching method(although it is sometimes said thatGA can not be used for finding the globaloptimization3).Here,the chromosomes generated by GA contain information about the image data,and the genetic and evolutionoperations are used to obtain the best match to the template:searching for the best match is the goal of this article.This thought emerged from the features of the GA,andthe need to recognize the faces of people easily and quicklyby an intelligent system.The single concept and features ofimage processing and the GA will not be introduced here,because there is already extensive literature on this subject.In this article,Sect.2 gives the encoding method of theGA and the experimental setting that is used.In Sect.3,theexperiment and the analysis are addressed.Some conclusions are given in Sect.4.Theory and experimental settingFor an image recognition system,the most interesting partthat has special features has first to be detected in the original image.This is called object detection.After that,thispart will be compared to a template to see if it is similar ornot.This is called object recognition.For example,if wewant to find a special person in an image,we first have todetect people in the image,and then recognize which one isthe person ofinterest(sometimes these two steps will beexecuted simultaneously).The whole procedure is shown inFig.1.Fig.1. Object recognition systemStatistical object recognition involves locating and isolating the targets in an image,and then identifying them bystatistical decision theory.One of the oldest techniques ofpattern recognition is matching filtering,4which allowsthe computation of a measure of thesimilarity between theoriginal image f(x,y)and a template h(x,y).Define themean-squared distancedxdy y x h y x f d fh ⎰⎰-=22))},(,({(1)dxdy y x h y x f R fh ),(),(⎰⎰=,if the image and template are normalized bydxdy y x h dxdy y x f),(),(22⎰⎰⎰⎰=(2) And thendxdy y x h y x f d fh 22)},(),({⎰⎰-= =dxdy y x h y x h y x f y x f ⎰⎰+-)},(),(),(2),({22=fh R dxdy y x f 2),(22-⎰⎰(3)For the right-hand side of Eq.3,the first term is constant,and thus fh R can measure as the least-squared similaritybetween the original image and the template.5If fh R has alarge value(which means that 2fh d is small enough),then theimage is judged to match thetemplate.If fh R is less than apreselected threshold,the recognition process will eitherrejectthe match or create a new pattern,which means thatthe similarity between the object in the original image andthe template is not satisfied.2.1 Genetic encodingAs introduced above,the chromosomes generated by theGA contain information about the image data,so the firststep is to encode the image data into a binarystring.6Theparameters of the center of a face(x,y)in the originalimage,the rate of scale to satisfy eq.2,and the rotatingan gleθare encoded into the elements of a gene.Som e important parameters of the GA used here are given inTable 1,and the search field and region are given in Table2.As shown in Table2,one chromosome contains 4 bytes:the coordinate(x,y)in the original image,the rate of scale,and the rotation angleθ.2.2 Experimental settingThe experiment is done by first loading the original and thetemplate images.The GA is used to find whether or notthere is the object of a template in the original image.If theanswer isYES,then in the original image the result givesthe coordinates of the center of the object,the scale,and therotation angle from the template.For comparison,thegeneral template matching method is also presented.7The execution time shows the effectiveness of the GA-based recognition method.Figures 2 and 3 are the original images and the templatesfor the experiment.The values are the width×height inpixels of the image.In Fig.2,three images are presented,the content and size of which are different.Figure 2a hastwo faces(the faces of a person and a toy),Fig.2b shows aface tipped to one side,and the person in Fig.2c wears a hatand the background is more complicated than in Figs.2a and b.The two templates in Fig.3 are not extracted from thesame image.For normal use,the template should be extracted as the average of several feature images.In Fig.4,the template(a)-0 is generated from(a)-1,(a)-2,and(a)-3,and takes the average value of the gray levels from the threemodels.The same is also true for(b)-0.Fig.2.Three original images(max_x×max_y).a 238×170.b 185×196.c 275×225Fig.3.Templates for matching(temp_x ×temp_y).a Template 1.b Template 23 Experiment and comparisonThe genetic operations and GA parameters are presentedin Table 1 and Table 2.The fitness is defined as255)_()_(),(),,,(0.1_0_0⨯⨯--=∑∑==y temp x temp j i temp rate y x f fitness y temp j x temp i θ(4)In Eq.4,),(j i temp is the gray level of the coordinates ),(j i in the template image,the width and height of which are x temp _ and y temp _.),,,(θrate y x f gives the gray level in theoriginal image,the coordinates of which are calculated bytranslation from ),(y x ,and by changing the scale and the rotation angleθfrom the template.Since the images are256gray-level images,in Eq.4,division by 255 ensures that theresulting fitness is between 0 and 1.The maximum numberof generations is limited to 300,and the threshold of thematching rate is set to 0.9.6That is to say,if within 300generations the matching rate can reach 0.9,then it is saidthat the template is found in the original image(the template matched the original image by thethreshold).Otherwise,the result gives the best match until the trainingreaches the 300th generation.The results of GA-based face recognition are given inFig.6 and Table 3.Figure 6a,c and d are searched to matchthe template Fig.3a,while Fig.6b is matched to Fig.3b.Figure 6a and b reach the matching rate 0.9 within 300generations,while Fig.6c and d cannot reach the matchingrate 0.9 within 300 generations(the best match is given inTable 3).In the images in Fig.6a–c,we see that the resultgiven matches the template well.The coordinates x,therate of scale,and the angle of rotationθhave been foundcorrectly,but for(y),Fig.6d,the result is not very satisfactory.The reason for this is that the template Fig.3a cannotrepresent the face of interest at all times.That is to say,although the person to be recognized in different imagesis the same,the template cannot give the features for thisperson at all times(different appearance,etc.),and in allconditions.(The creation of the template is shown in Fig.4.)A second reason is that the algorithm itself has some problems.For example,by using a GA-based recognitionmethod,the settings of the search field(in this paper,)yx is selected),the determination of the geneticrate,(,,operations,and the selection and optimization of the fitness function all have a strong effect on the level of recognition of theresultant image.Fig.4.Creation of templateFor the purpose of comparing the effects of the GA-based algorithm,the result of the general matching method7is also presented.From Fig.5,we see that although both theoriginal image(the top-left image)and the template(thetop-right image)are simplified by binarization,the matching time is 1 min 22 s.The recognizable result is the bottomleft image in Fig.5.Fig.5.Result of searching by a GA4 ConclusionsIn this article,the GA-based image recognition method istested,and a comparison with the general matching methodis presented.As we know,the GA starts with an initial set of randomsolutions called thepopulation.Each individual in thepopulation is called a chromosome,and represents a solution to the problem.By stochastic search techniques basedon the mechanism of natural selection and natural genetics,genetic operations(crossover and mutation)and evolutionaryoperations(selecting or rejecting)are used to search forthe best solution.8In this article,the chromosomes generated by the GAcontain information about the image,and we use the genetic operators to obtain the best match between the originalimage and the template.The parameters are the coordinates(x,y)of the center of the object in the original image,the rate of scale,and the angle of ro tationθ.In fact,translation,scale,and rotation are the three maininvariant moments in the field of pattern recognition.9However,for face recognition,the facial features are difficult toextract,and are calculated by the general pattern recognition theory and method.10Even these three main invariant moments will not be invariant because the facial expressionis changed in different images.Thus,recognition only gives the best matching resultwithin an upper predetermined threshold.Both the GA-based method and the general template matching methodare presented here,and the comparison with the traditionalpattern matching method shows thatthe recognition is satisfactory,although under some conditions the result is notvery good(Fig.6d).Based on the results of the experiments described here,future workwillemphasize(i)optimizing the fields of chromosomes,and(ii)improving the fitness function by addingsome terms to it.This work is important and necessary inorder to improve the GA-based face recognition system.References1.Sugisaka M,Fan X(2002)Development of a face recognitionsystem for the life robot.Proceedings of the 7th InternationalSymposium on Artificial Life andRobotics,Oita,Japan,vol 2,Shubundo Insatsu Co.Ltd.,pp 538–5412.Castleman K(1998)Digital image processing.Original editionpublished by Prentice Hall;a Simon&Schuster Press of TsinghuaUniversity,China3.Iba H(1994)Foundation of genetic algorithm:solution of mysticGA(in Japanese).Omu Press4.Deguchi K,Takahashi I(1999)Image-based simultaneous controlof robot and target object motion by direct-image interpretation.Proceedings of the 1999 IEEE/RSJ International Conference onIntelligent Robot and Systems,Kyongju,Korea,vol 1,pp 375–3805.Jaehne B(1995)Digital image processing:concepts,algorithms,and scientific applications,3rd edn.Springer Berlin,Heidelberg,Germany6.Agui T,Nagao T(2000)Introduction to image processing usingprogramming language C(in Japanese).Shoko-do Press7.Ishibashi’s studying room of C++(inJapanese)./ishidate/vcpp.htm8.Gen M,Cheng R(1997)Genetic algorithms and engineeringdesign.Wiley-Interscience,New York9.Agui T,Nagao T(1992)Image processing and recognition(inJapanese).Syokoudou Press10.Takimoto H,Mitsukura T,Fukumi M,et al.(2002)A design of aface detection system based on the feature extraction method.Proceedings of the 12th Symposium onFuzzy,Artificial Intelligence,Neural Networks and ComputationalIntelligence,Saga,Japan,pp 409–410中文译文对于脸部识别系统研究遗传算法基于计算机视觉的手势识别对于当今智能控制起着非常重要的作用。

一种改进的遗传算法梁影;金铭;乔晓林【摘要】In order to avoid premature convergence, an improved genetic algorithm (IGA) is presented. The algorithm adopts orthogonal mutation operator and Multi-locus mutation operator that improve the ability of global optimization. The results of simulation experiments show the efficiency of the Improved Algorithm. Compared with standard genetic algorithms, the improved algorithm has a higher degree of convergence, and to a certain extent, overcome the premature convergence.%针对遗传算法( Genetic Algorithm,GA)存在的未成熟收敛现象,提出一种改进的遗传算法(IGA).该算法采用双变异算子,即正交变异和多位点变异两种变异算子联合作用,提高了算法的全局寻优能力.仿真实验表明,对遗传算法的改进是有效的.改进后的算法与标准遗传算法相比具有更高的全局收敛性,并在一定程度上克服了未成熟收敛.【期刊名称】《科学技术与工程》【年(卷),期】2012(012)015【总页数】5页(P3636-3639,3644)【关键词】遗传算法;双变异算子;正交变异;全局收敛性【作者】梁影;金铭;乔晓林【作者单位】哈尔滨工业大学,哈尔滨150001;哈尔滨工业大学,哈尔滨150001;哈尔滨工业大学,哈尔滨150001【正文语种】中文【中图分类】TP301.6遗传算法［1］自20世纪70年代提出以来，因其在求解各类复杂问题过程中表现出的简单易行、全局寻优、鲁棒性强等特点日益受到人们的关注，现已成为研究的热点，目前在很多领域都有成功的应用。

电气工程的外文文献(及翻译)文献一：Electric power consumption prediction model based on grey theory optimized by genetic algorithms本文介绍了一种基于混合灰色理论与遗传算法优化的电力消耗预测模型。

该模型使用时间序列数据来建立模型，并使用灰色理论来解决数据的不确定性问题。

通过遗传算法的优化，模型能够更好地预测电力消耗，并取得了优异的预测结果。

此模型可以在大规模电力网络中使用，并具有较高的可行性和可靠性。

文献二：Intelligent control for energy-efficient operation of electric motors本文研究了一种智能控制方法，用于电动机的节能运行。

该方法提供了一种更高效的控制策略，使电动机能够在不同负载条件下以较低的功率运行。

该智能控制使用模糊逻辑方法来确定最佳的控制参数，并使用遗传算法来优化参数。

实验结果表明，该智能控制方法可以显著降低电动机的能耗，节省电能。

文献三：Fault diagnosis system for power transformers based on dissolved gas analysis本文介绍了一种基于溶解气体分析的电力变压器故障诊断系统。

通过对变压器油中的气体样品进行分析，可以检测和诊断变压器内部存在的故障类型。

该系统使用人工神经网络模型来对气体分析数据进行处理和分类。

实验结果表明，该系统可以准确地检测和诊断变压器的故障，并有助于实现有效的维护和管理。

文献四：Power quality improvement using series active filter based on iterative learning control technique本文研究了一种基于迭代研究控制技术的串联有源滤波器用于电能质量改善的方法。

附录4一种基于树结构的快速多目标遗传算法介绍：一般来讲，解决多目标的科学和工程问题，是一个非常困难的任务。

在这些多目标优化问题(MOPS)中，这些目标往往在一个高维的问题空间发生冲突，而且多目标优化也需要更多的计算资源。

一些经典的优化方法表明将多目标优化转化成为单目标优化问题，其中许多运行被要求找到多个解决方案。

这使得一种算法返回一组候选解，这比只返回一个基于目标的权重解的算法更好。

由于这个原因，在过去20年中，人们越来越感兴趣把进化算法(EAs)应用到多目标优化中。

许多多目标进化算法(MOEAs)已经被提出，这些多目标进化算法使用Pareto占优的概念来引导搜索，并返回一组非支配解作为结果。

与在单目标优化中找到最优解作为最终的解不同，在多目标优化中有二个目标：（1）收敛到Pareto最优解集（2）在Pareto最优解集中保持解的多样性。

为了解决在多目标优化中这两个有时候会冲突的任务，许多策略和方法被提出。

这些方法的一个共同的问题是，它们往往是错综复杂的。

对于这两项任务，为了得到更优秀的解，一些复杂的策略通常被使用，并且许多参数需要依据经验和已经得到的问题信息进行调整。

另外，许多多目标进化算法有高达()2GMNO的计算复杂度或者需要更多的处理时间（G是代数，M是目标函数的数量，N是种群大小。

这些符号在下文也保持相同的含义）。

在这篇文章中，我们提出了一种基于树结构的快速多目标遗传算法。

(这个数据结构是一个二进制树，它保存了在多目标优化中解的三值支配关系（例如，正在支配、被支配和非支配），因此，我们命名它为支配树(DT)。

由于一些独特的性能，使支配树能够含蓄地包含种群个体的密度信息，并且很明显地减少了种群个体之间的比较。

计算复杂度实验也表明，支配树是一种处理种群有效的工具。

基于支配树的进化算法(DTEA)统一了在支配树中的收敛性和多样性策略，即多目标进化算法中的两个目标，并且由于只有几个参数，这种算法很容易操作。

一种改进的遗传算法及其应用排新颖;马善立【摘要】Practical application of genetic algorithm is easy to premature convergence and accuracy of search results is not high. Optimal value for the premature convergence and low accuracy to dynamically adjust the search parameters to optimize the calculation , the whole process of evolution , the algorithm always maintain a strong global search ability and local search capabilities; test results show that genetic such improved algorithm is effective, easy to fall into local optimum, and can greatly improve the accuracy of the optimal solution.%遗传算法在实际应用中容易出现早熟收敛和搜索结果精度不高的问题.针对早熟收敛和最优值精度低,采用了对搜索参数进行动态调整的优化计算.在进化的全过程中,算法始终保持较强的全局搜索能力和局部寻优能力.测试结果表明,对遗传算法的此种改进是有效的,不易陷入局部最优,并能大大提高最优解的精度.【期刊名称】《科学技术与工程》【年(卷),期】2011(011)020【总页数】3页(P4836-4837,4842)【关键词】遗传算法;实数编码;过早收敛【作者】排新颖;马善立【作者单位】中国石油大学(华东)数学与计算科学学院,青岛,266555;中国石油大学(华东)数学与计算科学学院,青岛,266555【正文语种】中文【中图分类】O231遗传算法将生物的演化过程看作一个长期的优化过程，利用生物演化的思想去解决复杂的问题，这样不必精确地描述问题的全部特征，对优化对象没有可导、连续等要求;采用基于种群的搜索机制，强调个体之间的信息交换，只需根据优胜劣汰的自然法则产生新的更优解。