Genetic Algorithm Optimized Neural Networks Ensemble for Estimation of Mefenamic Acid and P
遗传算法
1 遗传算法1.1 遗传算法的定义遗传算法(GeneticAlgorithm，GA)是近多年来发展起来的一种全新的全局优化算法，它是基于了生物遗传学的观点，是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。
它通过自然选择、遗传、复制、变异等作用机制，实现各个个体的适应性的提高，从而达到全局优化。
遗传算法151解决一个实际问题通常都是从一个种群开始，而这个种群通常都是含有问题的一个集合。
这个种群是由一定数目的个体所构成的，利用生物遗传的知识我们可以知道这些个体正好组成了我们知道的染色体，也就是说染色体是由一个个有特征的个体组成的。
另外我们还知道，遗传算法是由染色体组成，而染色体是由基因组成，可以这么说，基因就决定了个体的特性，所以对于遗传算法的最开始的工作就需要进行编码工作。
然后形成初始的种群，最后进行选择、交叉和变异的操作。
1.2遗传算法的重要应用在现实应用中，遗传算法在很多领域得到很好的应用，特别是在解决多维并且相当困难的优化问题中时表现出了很大的优势。
在遗传算法的优化问题的应用中，其中最为经典的应用就是我们所熟悉的函数优化问题，它也是对遗传算法的性能进行评价的最普遍的一种算法;另外的一个最重要的应用，也就是我们本文所研究的应用—组合优化问题，一般的算法很难解决组合优化问题的搜索空间不断扩大的局面，而组合优化问题正好是解决这种问题的最有效的方法之一，在本文的研究中，比如求解TSP问题、VRP问题等方面都得到了很好的应用;另外遗传算法在航空控制系统中的应用、在图像处理和模式识别的应用、在生产调度方面的应用以及在工人智能、人工生命和机器学习方面都得到了很好的应用。
其实在当今的社会中，有关于优化方面的问题应用于各行各业中，因此有关于优化问题已经变得非常重要，它对于整个社会的发展来说都是一个不可改变的发展方向，也是社会发展的一个非常重要的需要。
1.3 遗传算法的特点遗传算法不同于传统的搜索与优化方法，它是随着问题种类的不同以及问题规模的扩大，能以有限的代价来很好的解决搜索和优化的方法。
heeds 遗传算法
heeds 遗传算法专业的设计优化工具领域，是最近二十多年才得以发展起来。
它代表了一种全新的设计仿真一体化的理念，使得设计的迭代大大加速。
而最新人工智能的算法，则使得这一进程，进一步被加速。
江湖鸣清雏1996年，一个华人软件工程师从通用电气的全球研究中心辞职出来，跟合伙人创立了Engineous Software公司，发布了软件iSight。
他在GE 领导软件开发，具有得天独厚的条件接触这些新技术。
GE在10%硬性淘汰的机制下，使得一帮子聪明人尝试各种新方法，以证明自己的价值。
但这位华人可能没有想到这个公司在短短时间内就成为业界的领袖。
在这之前的优化工具面临很多工程实际的挑战，而他将当时领先的响应面模型方法和试验设计方法写到了软件里。
这些方法摆脱了传统优化对数学方程式的依赖，适合于面向仿真的设计。
尽管iSight也难以脱离原来软件工具的窠臼，比如要求用户是优化专家，很多新的方法其实存在理论上的缺陷等，但对于当时几乎没有工程上可用的优化软件的制造业，已经是一个突破。
凭借良好的商业运作和通用电气的背景，很快iSight得到了CAE仿真应用水平最高的汽车与航空巨头公司的青睐，在公司成立之初四年时间，公司增长了20多倍。
在实际应用中，iSight也意识到优化建立于仿真过程的集成，于是在2003年推出FIPER集成环境，使得各种不同的CAE仿真工具可以自动交换数据，便于实现由优化来驱动设计和分析的一体化，这让iSight如虎添翼，继续迅速发展。
然而2008年金融风暴来袭，CAD/CAE大鳄法国的达索，在2009年以四千万美元的低价将Engineous公司全部收购，将iSight改成Isight，纳入到Simula仿真的家族中。
自此再也鲜有Isight的消息和发展，一颗新星悄然退于幕后。
波音自研软件的锋芒外露波音公司非常擅长开发自研软件，最早的一批研究基于响应面模型优化算法的就有波音的工程师。
在航空航天领域，优化的需求非常强烈。
软件工程英文参考文献(优秀范文105个)
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遗传算法的发展历程
遗传算法的发展历程遗传算法(Genetic Algorithm, GA)是近年来迅速发展起来的一种全新的随机搜索与优化算法,其基本思想是基于Darw in的进化论和Mendel的遗传学说。
该算法由密执安大学教授Holland及其学生于1975年创建。
此后，遗传算法的研究引起了国内外学者的关注。
1967年，Holland的学生J.D.Bagley在博士论文中首次提出“遗传算法（Genetic Algorithms）”一词。
此后，Holland指导学生完成了多篇有关遗传算法研究的论文。
1971年，R.B.Hollstien在他的博士论文中首次把遗传算法用于函数优化。
1975年是遗传算法研究历史上十分重要的一年。
这一年Holland出版了他的著名专著《自然系统和人工系统的自适应》，这是第一本系统论述遗传算法的专著，因此有人把1975年作为遗传算法的诞生年。
Holland在该书中系统地阐述了遗传算法的基本理论和方法，并提出了对遗传算法的理论研究和发展极其重要的模式理论。
该理论首次确认了结构重组遗传操作对于获得隐并行性的重要性。
同年，K.A.De Jong完成了他的博士论文《一类遗传自适应系统的行为分析》该论文所做的研究工作，可看作是遗传算法发展进程中的一个里程碑，这是因为，他把Holland的模式理论与他的计算实验结合起来。
尽管De Jong和Hollstien一样主要侧重于函数优化的应用研究，但他将选择、交叉和变异操作进一步完善和系统化，同时又提出了诸如代沟等新的遗传操作技术。
可以认为，De Jong的研究工作为遗传算法及其应用打下了坚实的基础，他所得出的许多结论，迄今仍具有普遍的指导意义。
进入八十年代，遗传算法迎来了兴盛发展时期，无论是理论研究还是应用研究都成了十分热门的课题。
1985年，在美国召开了第一届遗传算法国际会议，并且成立国际遗传算法学会，以后每两年举行一次。
1989年，Holland的学生D.E.Goldberg出版了专著《搜索、优化和机器学习中的遗传算法》。
基于遗传算法优化BP神经网络圆柱壳结构可靠度分析
基于遗传算法优化BP神经网络圆柱壳结构可靠度分析目录一、内容概括 (1)（一）基于遗传算法的优化方法介绍 (2)（二）BP神经网络介绍与应用场景分析 (2)（三）圆柱壳结构可靠度分析方法探讨 (4)二、圆柱壳结构基础理论知识概述 (5)（一）圆柱壳结构的组成及特点分析 (6)（二）圆柱壳结构的力学特性研究 (7)（三）圆柱壳结构可靠度评价指标介绍 (9)三、BP神经网络在圆柱壳结构可靠度分析中的应用 (9)（一）BP神经网络模型的构建与训练过程 (10)（二）基于BP神经网络的圆柱壳结构可靠度预测模型建立与实施步骤介绍11 （三）BP神经网络模型的优缺点分析及对策建议 (13)四、遗传算法在优化BP神经网络模型中的应用 (14)（一）遗传算法的基本原理及特点介绍 (16)（二）基于遗传算法的BP神经网络模型优化过程与实施步骤解析..16（三）案例分析 (18)一、内容概括介绍了BP神经网络的基本原理及其在当前圆柱壳结构可靠度分析中的局限性。
BP神经网络是一种通过反向传播算法进行权值和阈值调整的多层前馈网络，广泛应用于各种工程领域。
传统的BP神经网络在解决复杂结构优化问题时，往往存在易陷入局部最优解、收敛速度慢等问题。
阐述了遗传算法的基本原理和特性，遗传算法是一种模拟自然选择和遗传机制的优化搜索算法，具有全局优化能力，能够解决复杂的非线性问题。
将遗传算法与BP神经网络相结合，有望提高圆柱壳结构可靠度分析的准确性和效率。
详细描述了基于遗传算法优化BP神经网络的流程和方法。
通过遗传算法优化BP神经网络的权值和阈值，提高网络的性能和准确性。
将优化后的BP神经网络应用于圆柱壳结构可靠度分析，通过大量的数据训练和测试，验证该方法的可行性和有效性。
通过实例分析，展示了基于遗传算法优化BP神经网络在圆柱壳结构可靠度分析中的实际应用效果。
该方法能够显著提高圆柱壳结构可靠度分析的准确性和效率，为工程实践提供了一种新的思路和方法。
基于遗传算法的组网雷达优化部署
关键词 ： 优化 ห้องสมุดไป่ตู้署 ； 遗 传 算 法 ；雷 达 组 网 ； 适 应 度 函数
Ａｂ ｓ ｔ ｒ ａ ｃ ｔ ： Ｔｈ ｅ ｏｐ ｔ ｉ ｍ ａｌ ｄ ｅ ｐｌ ｏｙｍ ｅ ｎ ｔ ｏｆ ｎ ｅ ｔ ｔ ｉ ｎｇ ｒ ａ ｄ ａ ｒ ｓ ｉ ｓ ｔ ｈｅ ｆ ｏ ｕｎｄ ａ ｔ ｉ ｏ ｎ ｔ ｈａ ｔ ｔ ｈｅ ｒ ａ ｄ ａ ｒ ｎｅ ｔ ｗｏｒ ｋ ｅ ｘｅ ｒ ｔ ｓ ｉ ｔ ｓ ｐ ｅ ｒ ｆ ｏ ｒ ｍａ ｎｃ ｅ ｔ Ｏ ｉ ｍ ｐｒ ｏ ｖｅ ｔ ｈｅ ｃ ａ ｐａ ｂｉ ｌ ｉ ｔ ｙ ｏｆ ｄｅ ｔ ｅ ｃ ｔ ｉ ｏｎ． Ｉ ｎ ｖｏ ｌ ｖｅ ｄ ｉ ｎ Ｓ Ｏ ｍ ａ ｎ ｙ ｆ ａ ｃ ｔ ｏｒ ｓ， ｔ ｈ ｅ ｏ ｐｔ ｉ ｍ ａｌ ｄｅ ｐｌ ｏ ｙｍ ｅ ｎｔ ｏｆ ｒ ａｄ ａ ｒ ｓ ｉ ｓ ａ ｍ ｕｌ ｔ ｉ ｔ ａ ｒ ｇｅ ｔ ａ ｎｄ ｍ ｕｌ ｔ ｉ — ｃ ｏ ｎｓ ｔ ｒ ａ ｉ ｎｔ ｉ ｓ ｓ ｕｅ ．Ｉ ｎ ｔ ｈｉ ｓ ｐａ ｐ ｅ ｒ，ａ ｎ ａ ｌ ｇ ｏｒ ｉ ｔ ｈｍ ｂａ ｓ ｅ ｄ ｏ ｎ ｇ ｅ ｎｅ ｔ ｉ ｃ ａ ｌ ｇｏ ｒ ｉ ｔ ｈｍ ｓ ｉ ｓ
Ｋｅ ｙ ｗｏ ｒ ｄ ｓ ：ｏ ｐ ｔ ｉ ｍａ ｌ ｄ ｅ ｐ ｌ ｏ ｙ ｍｅ ｎ ｔ ；ｇ ｅ ｎ ｅ ｔ ｉ ｃ ａ ｌ ｇ ｏ ｒ ｉ ｔ ｈ ｍｓ ；ｒ ａ ｄ ａ ｒ ｎ ｅ ｔ ｔ ｉ ｎ ｇ；ｆ ｉ ｔ ｎ ｅ ｓ ｓ ｆ ｕ ｎ ｃ ｔ ｉ ｏ ｎ
遗传算法优化svm参数
遗传算法优化svm参数遗传算法是一种基于自然选择和进化理论的优化算法，适用于求解复杂的非线性优化问题。
由于支持向量机（SupportVector Machine，SVM）在机器学习中被广泛应用于分类和回归问题，因此使用遗传算法来优化SVM的参数是一个常见的研究方向。
SVM是一种二分类模型，通过在特征空间中寻找最佳的超平面对数据进行分类。
根据问题的不同，SVM具有多个参数需要进行调优，包括C（正则化常数）和核函数中的参数等。
使用遗传算法来优化这些参数可以通过以下步骤实现：1. 确定问题的适应度函数：在遗传算法中，适应度函数用于评估每个个体的性能。
对于SVM参数优化问题，可以选择采用交叉验证准确率或分类精度作为适应度函数。
2. 初始化种群：在遗传算法中，初始化种群是一个重要的步骤。
对于SVM参数优化问题，可以随机生成一组初始参数作为种群的起始点。
3. 选择操作：选择操作是根据适应度函数的结果选择优秀的个体。
常用的选择算法有轮盘赌选择和锦标赛选择等。
4. 交叉操作：交叉操作是从选择的个体中随机选择两个或多个个体，通过某种方式进行交叉生成新的个体。
在SVM参数优化问题中，可以选择单点交叉、多点交叉或均匀交叉等策略。
5. 变异操作：变异操作是为了确保种群具有一定的多样性，防止算法陷入局部最优解。
在SVM参数优化中，可以通过改变个体的某个或多个参数的值来进行变异。
6. 评价和重复：每次进行选择、交叉和变异操作后，都需要对生成的新个体进行评价并计算适应度值。
重复上述步骤直到满足终止条件为止，比如达到最大迭代次数或适应度达到某个阈值。
在进行SVM参数优化时，有几个问题需要考虑：1. 参数范围：对于每个参数，需要明确其可能的取值范围。
例如，正则化常数C通常取值为0到无穷大之间的正实数。
2. 交叉验证：在SVM参数优化中，使用交叉验证是常见的一种方式。
通过将数据集划分为训练集和验证集，可以评估不同参数组合的性能。
常用的交叉验证方法有k折交叉验证和留一验证等。
基于遗传算法优化的BP神经网络在考研结果预测中的应用
黑铉语言信麵与电睡China Computer & Communication2021年第1期基于遗传算法优化的B P神经网络在考研结果预测中的应用李驰(四川大学锦城学院计算机科学与软件工程系，四川成都611731)摘要：通过遗传算法先对BP神经网络的初始权值和阈值进行优化后，再将BP神经网络用于考研结果的预测模型中。
实验表明，这种优化后的预测模型因为克服了收敛速度慢、易产生局部最小等缺陷，比单纯使用BP神经网络建立的预测 模型准确度更高。
将这个预测模型用于考研报名之前供学生预测参考，方便学生做出合理的决策，具有一定的实际意义。
关键词：考研；预测；BP神经网络；遗传算法中图分类号：TD712 文献标识码：A文章编号：1003-9767 (2021) 01-038-04Application of BP Neural Network Based on Genetic Algorithms Optimization in Prediction of Postgraduate Entrance ExaminationLI Chi(Department of Computer Science and Software Engineering,Jincheng College of Sichuan University,Chengdu Sichuan611731, China) Abstract：F irs tly,the in itia l weight and threshold of BP neural network are optimized by genetic algorithm,and then BP neural netw ork is used in the pre diction model of the results o f the postgraduate entrance exam ination.The experim ent shows that the optim ized prediction model overcomes the shortcomings o f slow convergence speed and easy to produce local m inim um,so it is more accurate than the pre diction model established by BP neural network alone.This pre diction model can be used as a reference for students to make a reasonable decision before applying fo r postgraduate entrance examination.Key words：postgraduate entrance exam ination;prediction;BP neural network;genetic algorithms〇引言随着社会对于高素质知识型人才的需求越来越迫切，我 国报考研究生的人数呈现逐年大幅増加的趋势。
GE遗传算法
1.2-3.2-5.3-7.2-1.4-9.7
物竞天择－－适应性评分与选择函数
1.适应度函数（fitness function）
适应函数基本上依据优化问题的目标函数而定。当适应函数 确定以后，自然选择规律是以适应函数值的大小决定的概率分布 来确定哪些染色体适应生存，哪些被淘汰。生存下来的染色体组 成种群，形成一个可以繁衍下一代的群体。
2.选择（tion）
常用的选择方法―轮盘赌选择法比如有5条染色体，他们所对 应的适应度评分分别为：5，7，10，13，15
P1
F1
5
10%
Fi 5 7 10 13 15
这个轮盘是按照各个个体的适应度比例进行分块的，适应度评分 越高的个体被选中的概率越大。
遗传变异――基因重组（交叉）与基因突变
遗传算法
Genetic Algorithm
能够生存下来的往往不是最强大的物种，也不是最聪明的物种，而是最能适应环 境的物种。
遗传算法的由来
• 遗传算法（Genetic Algorithm, GA）起源于对生物系统所进行 的计算机模拟研究。它是模仿自然界生物进化机制发展起来的随 机全局搜索和优化方法，借鉴了达尔文的进化论和孟德尔的遗传 学说。其本质是一种高效、并行、全局搜索的方法，能在搜索过 程中自动获取和积累有关搜索空间的知识，并自适应地控制搜索 过程以求得最佳解。
遗传算法的实现过程
• 首先寻找一种对问题潜在解进行“数字化”编码的方案。（建立表现型和基因 型的映射关系）
• 然后用随机数初始化一个种群（第一批袋鼠就被随意地分散在山脉上），种群 里面的个体就是这些数字化的编码。
• 接下来，通过适当的解码过程（得到袋鼠的位置坐标） • 用适应性函数对每一个基因个体作一次适应度评估（袋鼠爬得越高，适应度相