用遗传算法对自动控制系统数学模型寻优

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求，用遗传算法寻找出控制系统最佳稳定域，实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的价值，并用仿真实验对优化结果进行了检验。结果表明：在控制系统数学模型的优化中，遗传算法具有其他算法无可比拟的优越性。     

面波频散反演地球内部构造的遗传算法

介绍了一种新的算法--遗传算法的基本概念和特点，及其在地震面波反演地球内部构造中的应用，指出了使用遗传算法的注意事项．提出了通过对初步搜索结果参数分布直方图进行分析，从而修改和缩小进一步搜索的范围，逐步搜索以提高搜索效率的方法．并对３层含低速层的理论模型和青藏高原的实际频散资料进行遗传算法反演，获得了满意的结果．讨论了遗传算法在其他地震学问题中进一步应用的可能性．     

遗传算法在离散变量结构优化中的应用及其改进方案综述

简要介绍了遗传算法的基本原理，以及使用遗传算法进行离散变量结构优化的过程。针对传统的遗传算法在优化过程中的不足之处，介绍了几种改进方案，分别应用于遗传算法的编码、搜索、目标函数的生成等各个环节，以提高整个算法的执行效率。     

微种群遗传算法优化结构振动控制

本文将微种群遗传算法应用到结构振动控制中，用遗传算法优化控制器，以解决一类用经典线性反馈控制无法解决的半主动控制的优化问题，该方法提出将非线性控制问题线性化，导出了简化过程，然后利用遗传算法求解，它具有利用微种群遗传算法全局寻优，并且对目标函数的性态要求较少的特点，数值算例表明，本文方法是有效的。     

遗传算法在地球物理中的应用进展

简要介绍了遗传算法的基本原理及实现步骤；论述了遗传算法在地震数据处理中的AVO波、速度参数、静校正，波阻抗等方面的应用状况；概述了该方法在重力、电法、测井及磁力等非地震勘探的研究进展；最后讨论了遗传算法的优缺点，对遗传算法的发展及其在地球物理勘探中的进一步应用作了展望。     

遗传算法的改进及其在应力场反演中的应用

利用人工方法产生多样化的初始群体，引入“移民”机制并采用小种群搜索，运用自动调整交换概率与变异概率的方法将遗传算法进行了改进，改进后的遗传算法在防止早熟，提高收敛速度方面有一定改善。最后利用改进的遗传算法反演了青藏高原的应力场，其结果与地质结果有一定的相似性。     

波形反演遗传算法及其在地震测深数据解释中的应用

为了更好地利用地震测深波形数据，提出了地震体波波形反演的遗传算法. 正演使用能精确快速计算互层结构响应的广义反、透射系数理论地震图算法；反演采用遗传算法，实现了地震体波波形反演的遗传算法. 数值试验表明，该算法具有分辨壳内低速层、高低速薄互层结构和一定的抗噪能力. 青藏高原东北缘泽库、夏河、临洮3炮地震测深P波波形反演，得到了上地壳底部低速层和中、下地壳，以及上地幔顶部薄互层的细结构图象.      

遗传算法在确定震源位置中的应用

地震定位需要震源的空间坐标和发震时刻，可以通过使观测走时和计算走时的拟合差达到最小来实现。遗传算法是解决此最优化问题的有效方法。本文对遗传算法确定震源位置的基本思路思路进行了说明和分析，讨论了拟合差随迭代次数的分布特征，最后，给出了最优化过程搜索震源位置在四维空间的分布。     

用遗传算法实现地震信号反褶积

遗传算法作为寻优手段具有全局优化和很好的稳定性．本文将遗传算法用于地震信号反褶积处理，与已往方法相比它具有更好的分辨率和稳定性我们采用Bernoulli－Gaussian模型和ARMA模型分别描述地震反射系数序列和地震子波，用最大似然和最小预测误差准则分别构造用于估计反射系数序列和地震子波的目标函数，用遗传算法优化目标函数，以实现地震信号反褶积．     

电测深曲线的遗传算法反演

电测深曲线作为地下介质电阻率和深度的非线性函数，其解具有高度的非唯一性．常规的基于局部线性化的最优化反演方法易使解估计陷入局部极大值中，而且严重地依赖初始模型的选择．遗传算法作为一种全局最优化方法，对初始模型的依赖性大为减弱，且不易陷人局部极大值之中，从而能有效地解决这类非线性最优化问题．本文首次将遗传算法用于电测深解释并对实测曲线进行反演，效果很好，显示了遗传算法独特的优越性．     

结合灵敏度修正的遗传算法的结构损伤诊断

提出了1种结合灵敏度修正的遗传算法进行结构损伤诊断。在遗传算法计算过程中加入灵敏度修正操作,使遗传过程得以快速收敛并增加了识别准确性。利用4层平面框架进行数值模拟,识别结果表明,本文所提出的结构损伤识别方法比常规遗传算法有效。     

基于粗粒度并行遗传算法的隔震层参数优化

针对基于经典遗传算法的隔震层参数优化方法效率不高的问题,提出一种基于粗粒度并行遗传算法的隔震层参数优化方法。利用Python的多进程机制和Python与ETABS的交互,实现CPU各核同时调用ETABS并进行遗传操作,最后通过一个隔震工程的实例进行验证。结果表明：采用粗粒度并行遗传算法进行隔震层参数优化,与原设计结果相比,优化后的隔震结构性能更优;同时,用10核CPU计算,与经典遗传算法相比,该方法既能准确得出全局最优解,又可显著提高优化效率,加速比约为6,可基本满足隔震工程设计的及时性需求,具有较好的工程应用价值。     

1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach

Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step.     

遗传算法及其在速度结构与震源联合反演中的应用

遗传算法是近年来发展较快的一种求解多参数非线性优化问题的有效方法。本文通过对遗传传算法的介绍。对该方法的发展现状及特点进行了分析,在此基础上,对遗传算法进行了改进：通过引入放大系统Ｋ。对目标函数进行压缩及扩展来提高遗传算法的搜索机制;     

基于经验遗传-单纯形算法和结构模态参数识别结构物理参数的方法

对于复杂的大自由度系统的反演分析,遗传算法每步计算中包含大量的正演分析,成为限制遗传算法应用的运行速度的瓶颈。减少反演分析中的正演计算次数,是扩大遗传算法适用范围的有效途径。经验遗传-单纯形算法正是解决这一问题的一种有效方法。本文将这一方法应用于不完全模态参数已知条件下的结构物理参数识别研究。结果表明:本文建议的方法有精度和搜索效率高、对初值选取依赖性不强、可以反映"残缺"的高阶模态信息等优点。     

Hybrid genetic algorithms in view of the evolution theories with application for the electrical sounding method

The classical genetic algorithm is a stochastic process which operates by natural selection. Although the algorithm may localize a point around the global minimum of the misfit function, it is not efficient at finding the precise solution. This paper suggests some hybrid genetic algorithms, derived from evolution theories, to overcome this problem. Firstly, sexual selection has been incorporated in the classical genetic algorithm to obtain a full representation of the Darwinist evolution concept. The simulation of sexual selection is performed by assigning a higher probability of surviving to some parameters that satisfy some algebraic relationships. This method is called the ‘marked constraints’ algorithm since it permits us to insert geological and geophysical constraints into the problem. The algorithm implementation is realized by progressively shrinking the parameter search space through successive generations. In this way, the genetic algorithm gains some degree of determinism. Secondly, since the evolution theory of Lamarck postulates that the acquired traits are passed on to the next generation, a hybrid use of the damped least‐squares method and the genetic algorithm is called Lamarckian inversion. Lamarckian inversion involves some improvement procedures that simulate the reduction of the misfit with the help of a derivative‐based method between two generations. Finally, although there is no correspondence in nature, Lamarckian and Darwinist evolution concepts are combined to strengthen the deterministic part of the solution algorithm. This is called the Lamarckian‐marked‐constraint algorithm. The merits and behaviours of the suggested algorithms are discussed using two examples. The first is a hypothetical example affected by a multiminima problem. The second examines the equivalence problem using vertical electrical sounding data.     

Optimal design of soil dynamic compaction using genetic algorithm and fuzzy system

Computational intelligent techniques, such as fuzzy and genetic algorithm, have proven to be useful in modeling of complex nonlinear phenomena such as dynamic compaction. Dynamic compaction method is used to improve the mechanical behavior of underlying soil layers especially loose granular materials. The method involves the repeated impart of high-energy impacts to the soil surface using steel or concrete tampers with weights ranging 10–40 ton and with drop heights ranging 10–40 m. A relatively exact estimation of dynamic compaction level is of major concern to geotechnical engineers. This paper develops a fuzzy set base method for the analysis of dynamic compaction phenomenon. In this model, the input variables are tamper weight, height of tamper drop, print spacing, tamper radius, number of impact and soil layer geotechnical properties. The main shortcoming of this technique is uncertainty to locate the best sketch of dynamic compaction to gain maximum effect of this method of soil improvement. Therefore, this paper describes the incorporation of genetic algorithm methodology using fuzzy system for determining the optimum design of dynamic compaction. Subsequently, it will be shown that the genetic algorithm has some abilities in the optimization of dynamic compaction design. Also different manners of this algorithm are compared and then the optimized structure of genetic algorithm will be suggested for dynamic compaction.     

关于-遗传算法收敛性的注记

遗传算法是一种受到广泛注意的全局优化算法,已经在包括地震工程的很多领域中获得应用.本文将结合这一算法的实际操作步骤,对简单遗传算法的不收敛性和包含最优个体保护策略的遗传算法的收敛性给出一个简要的证明.     

量子遗传算法在大地电磁反演中的应用

量子遗传算法（QGA）以量子理论为基础,通过利用量子位编码代替经典遗传算法的二进制位编码,利用量子旋转门定向更新种群来代替传统方法中种群的选择、交叉和变异过程,使得算法具有一定的内在并行运算能力和量子的隧道效应,从而加快了搜索速度,改善了收敛速度,并具有更强的全局寻优能力.本文针对地球物理反演问题的非线性、多极值特点提出一套实现方案,通过理论模型和实测数据试验对比研究,表明量子遗传方法在大地电磁反演中的寻优质量和效果明显优于传统遗传算法.