Interactive Genetic Algorithms with Fitness Adjustment

Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.

Improved particle filtering techniques based on generalized interactive genetic algorithm

This paper improves the resampling step of particle filtering（PF） based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage.For target tracking in image processing,this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image.The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics,and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation,and non-uniform mutation operator is used to capture all kinds of mutation in this paper.The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering（EKF）,PF,regularized partide filtering（RPF）,and genetic algorithm（GA）-PF.

Design Method for Optimizing the Interactive Interface of Live Broadcasting Platform for the Elderly Users

In the era of network live broadcasting for everyone,the development of live broadcasting platforms is also more intelligent and diversified.However,in the face of a large group of elderly users,the interface interaction design mode used is still mainly based on the interaction mode for young groups,and is not designed for elderly users.Therefore,a design method for optimizing the interaction interface of live broadcasting platform for elderly users was proposed in this study.Firstly,the case study method and Delphi expert survey method were used to determine the design needs of elderly users and the design mode was analysed.Secondly,the orthogonal design principle was used to design a test sample of the interactive interface of live broadcasting platform applicable for the elderly users,and then a user evaluation system was established to calculate the weights of the design elements using hierarchical analysis,and then the predictive relationship between the design mode of the interactive interface of live broadcasting platform and the elderly users was established by Quantitative Theory I.Finally,Genetic Algorithm was applied to generate the optimized design scheme.The results showed that the design method based on the Genetic Algorithm and the combination of Quantitative Theory can scientifically and effectively optimize the design of the interactive interface of the live broadcasting platform for the elderly users,and improve the experience of the elderly users.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

基于数量化一类分析的IGA算法及应用

针对交互式遗传算法(IGA)中用户易疲劳的问题,引入数量化一类分析方法,从提高收敛速度和模拟人工评价两方面入手,改善IGA性能.采用均匀设计法确定遗传算法的初始种群,使得各基因单元类型在初始种群中均匀分布,从而提高遗传算法全局最优解的收敛性能;利用数量化一类分析,求取各基因单元类型对适应度评价的贡献权值,指导GA选择、交叉、变异等操作,以加速算法收敛;在GA操作后期,利用各基因单元类型对适应度评价的权值,模拟个体适应度的自动评价,降低用户疲劳度和提高效率.将该方法应用于动漫玩具形态造型中.结果表明,采用该方法可以有效地提高收敛速度和降低用户疲劳度.

基于IGA的知识检索需求获取方法

针对知识检索过程中用户需求模糊、不充分而难以准确获取的问题,文章通过分析用户需求的特点,将其区分为显性和隐性2部分,并结合概念图的思想设计了一种需求语义描述模型;以此为基础,结合交互式遗传算法的基本原理,探讨了交互式遗传算法在知识检索需求获取过程中的应用模式,并通过实验验证了模型及方法的可行性。

基于IGA的板形板厚神经网络分散解耦PID控制

针对板带材轧制是一个复杂的非线性过程 ,板形控制 (AFC)和板厚控制 (AGC)又是相互耦合的一个综合系统等特点 ,该文首先采用神经网络分散解耦方法 ,对此板形板厚多变量耦合系统进行解耦 ,而后再应用基于免疫遗传算法的PID控制对解耦后的已近似成为两个独立的SISO系统的广义对象进行控制 ,从而建立了基于免疫遗传算法的板形板厚神经网络分散解耦PID控制系统。仿真结果证明了此AFC -AGC控制系统具有良好的自适应跟随和抗扰性能 ,其控制效果优于传统的解耦PID控制。

基于IGA-LSSVM的煤矿瓦斯涌出量预测模型研究

为有效提高煤矿瓦斯涌出量预测的准确性,进一步保障煤矿生产安全,提出经免疫遗传算法(IGA)优化的加权最小二乘支持向量机(LS-SVM),并用其建立煤矿瓦斯涌出量预测模型。首先针对瓦斯涌出量系统非线性、时变性、复杂性等特点,提出一种新的加权策略函数来改进LS-SVM。然后引入IGA,对改进的LS-SVM进行核参数δ和正则化参数γ寻优。最后,利用煤矿历史瓦斯涌出数据进行试验分析。结果表明,利用该模型预测的最大相对误差为2.763%,最小相对误差为0.705%,平均相对误差为1.329 8%,该模型较其他预测模型具有更快的收敛速度,更强的泛化能力和更高的预测精度。

基于优化IGA-ELM模型的爆破振动特征参量预测研究

为有效预测露天矿爆破振动特征参量,建立基于组合赋权的免疫遗传算法(IGA)优化极限学习机(ELM)预测模型。建立该模型之前,根据爆破振动影响因素确定输入层参数,根据爆破安全规程判据确定输出层参数;然后,应用调和平均数概念整合模糊层次分析法(FAHP)所得主观权重和熵权法所得客观权重,量化输入层参数权重;其次,针对现有ELM输入层权值、隐含层偏差的选择问题,引入IGA对其进行优化选择,并通过逐步增减法探究ELM隐含层最优节点数。该模型曾被应用于某露天矿。研究结果表明:用所构建优化IGA-ELM模型能够更准确地预测露天矿爆破振动特征参量,且所得均方误差、决定系数、仿真误差明显优于其他模型。

基于SVM-IGA的补料分批发酵过程优化控制

针对非线性时变的发酵过程,建立了用于产物浓度预估的支持向量机(SVM)模型。在此模型基础上,利用免疫遗传算法(IGA)实现对发酵过程补料优化控制参数的寻优;实验结果表明,该方法可行,且能提高产物的产量;所提出的这种方法是对解决补料分批发酵过程优化问题的一个新尝试。

基于IGA-SVM的发酵过程建模及优化控制

利用免疫遗传算法(IGA,Immune Genetic Algorithm)的全局搜索功能和支持向量机(SVM,Support Vector Machine)泛化能力强的特点,选择合适的状态变量,对发酵过程建立动态时变模型。利用该模型和算法对一些不能在线测量的生化状态变量进行在线预估,并对一些关键的操作变量进行了优化。通过对谷氨酸发酵过程的实际应用,验证了该方法的有效性。

基于IGA-BP神经网络滚动轴承故障的诊断

为提高滚动轴承故障模式识别技术的研究,基于IGA-BP神经网络的故障诊断原理,运用IGA对BP神经网络的权值与阈值进行调整和优化,利用小波包分解获得轴承振动信号的特征向量,进行了滚动轴承故障的诊断实验研究,对故障模式进行识别。结果表明,IGA-BP神经网络方法具有很强的故障识别能力,说明利用IGA-BP神经网络方法进行轴承故障诊断是可行的。

基于IGA的支持向量机特征子集选择和参数优化

特征子集选择和训练参数的优化一直是SVM研究中的两个重要方面,选择合适的特征和合理的训练参数可以提高SVM分类器的性能,以往的研究是将两个问题分别进行解决。随着遗传优化等自然计算技术在人工智能领域的应用,开始出现特征选择及参数的同时优化研究。研究采用免疫遗传算法(IGA)对特征选择及SVM参数的同时优化,提出了一种IGA-SVM算法。实验表明,该方法可找出合适的特征子集及SVM参数,并取得较好的分类效果,证明算法的有效性。

优化中医八纲辨证神经网络参数的MIGA-1算法

从分析中医八纲辨证神经网络的不足出发,在基于Meta种群理论免疫遗传算法(MIGA)的基础上设计了一种优化中医八纲辨证神经网络参数的免疫遗传算法——MIGA-1优化算法。实验结果表明,以MIGA-1算法作为参数优化机制的中医八纲辨证神经网络具有更好的自我适应能力,减少了人为设定参数的随意性。

用IGA优化的直流电机的仿人智能控制

讨论了当前足球机器人运动控制系统的控制算法对足球机器人运动性能的影响。在分析了足球机器人运动控制系统组成和电机数学模型的基础上,对足球机器人运动控制器采用多模态控制的仿人智能控制(Human-Simulated Intelligent Control,HSIC)算法,利用改进的遗传算法(Improved Genetic Algorithm,IGA)对仿人智能控制器参数进行优化。通过与目前普遍采用的常规PID控制器作对比实验,表明采用IGA参数整定后的HSIC控制器对电机具有更好的控制品质,并改善了足球机器人的运动性能。

WPA-IGA-BP神经网络的模拟电路故障诊断

针对模拟电路渐变性故障中的特征提取困难和故障信号无法进行有效分类的问题,提出利用免疫遗传算法(immune genetic algorithm,IGA)优化反向传播(back propagation,BP)神经网络中参数寻优过程,从而实现模拟电路故障诊断。首先,采用小波包分析(wavelet package analysis,WPA),对模拟电路输出响应进行4层小波分解和重构,完成特征向量的提取。然后,采用IGA优化BP神经网络进行训练及测试,实现对不同故障进行故障诊断。最后,通过两个模拟电路仿真实验对该方法进行实验验证。实验结果表明,与优化前的BP神经网络相比,所提方法提高故障诊断的准确率约15%。

基于IGA的漂白过程温度优化控制

针对工艺复杂的造纸过程漂白工段的温度控制优化问题,分析了漂白过程温度控制优化策略,提出了标准遗传算法的改进方法,采用基于改进型遗传算法(IGA)整定PID控制器的方法实现了对漂白塔温度的优化控制,并介绍了基于DCS的优化控制系统配置。仿真结果和实践证明了基于IGA的漂白过程温度控制优化方案的正确性和有效性。

带调控机制和SVM的IGA的在人脸图像检索的应用

在目标图像未知的情况下,人脸图像检索具有特殊性。用户要在人脸图像库中找出符合某些特征的图像,比如找出与记忆吻合或者吻合度高的某个图像,这就要求问题的解必须是全局最优的,并且不允许丢失最优解。本文提出一种带调控机制和支持向量机的交互式遗传算法来防止最优解丢失,加速算法收敛,减轻用户疲劳从而改善和提高检索性能。

基于IIGA-BP神经网络的钢材销售预测模型

为克服传统BP神经网络(BP Neural Network, BPNN)在销售预测中,预测精度低、收敛速度慢的缺点.提出了一种基于改进免疫遗传算法(Improved Immune Genetic Algorithm, IIGA)优化BP神经网络的销售预测模型.改进的免疫遗传算法提出了新的种群初始化方式、抗体浓度的调节机制及自适应交叉算子、变异算子的设计方法,有效的提高了IIGA的收敛能力和寻优能力.并用IIGA优化BPNN的初始权值和阈值,改善网络参数的随机性导致BPNN输出不稳定和易陷入局部极值的缺点.以某钢铁企业的历史销售数据为例进行实证研究,利用Matlab分别构建BP、IGA-BP和IIGA-BP神经网络预测模型进行仿真对比分析.实验证明, IIGA-BP神经网络预测模型较BP神经网络预测模型预测精度提高了23.82%,较IGA-BP神经网络预测模型预测精度提高了22.02%.IIGA-BP神经网络模型对钢材销售预测的泛化性能更好,预测效果更稳定误差基本保持在[-0.25, 0.25]之间,预测精度大幅度提高,为企业销售预测提供了一种较为有效的方法.