Weighted edit distance optimized using genetic algorithm for SMILES-based compound similarity

Pattern Analysis and Applications - A method for developing new drugs is the ligand-based approach, which requires intermolecular similarity computation. The simplified molecular input line entry...     

Multi-hop communications on wireless network-on-chip using optimized phased-array antennas

Network-on-Chip (NoC) as a promising design approach for on-chip interconnect fabrics could overcome the energy as well as synchronization challenges of the conventional interconnects in the gigascale System-on-Chips (SoC). The advantages of communication performance of traditional wired NoC will no longer be continued by the future technology scaling. Packets that travel between distant nodes of a large scale wired on-chip network significantly suffer from energy dissipation and latency due to the routing overhead at each hop. According to the International Technology Roadmap for Semiconductors annual report, the RFCMOS characteristics will be steadily improved by technology scaling. As the operating frequency of RF devices increases, the size of Si integrated antenna will decrease and it is feasible to employ them as a revolutionary interconnect for intra-chip wireless communications. In this paper, we focus on physical requirements and design challenges of wireless NoC. It is demonstrated that employing an optimum-radiation phased array antenna and multihop communications will increase the reliability of on-chip wireless links by several orders of magnitude using a limited power budget less than 0.1 pJ/bit.     

利用遗传算法优化的支持向量机垃圾邮件分类

提出一种利用遗传算法优化支持向量机来进行垃圾邮件的分类方法.首先对邮件进行预处理,然后利用遗传算法优化支持向量机的惩罚因子和核函数参数的组合,最后利用优化后的支持向量机对邮件进行分类.在由5800篇邮件构成的数据集上进行实验的结果表明,该方法能达到89.67%的准确率,提高了对中文垃圾电子邮件过滤的准确性.     

A second generation current conveyor based PID controller optimized using a crossover improved genetic algorithm

This article presents a second generation current conveyor (CCII) based PID controller that has been optimally designed using a crossover improved genetic algorithm (CIGA). PID controller is widely adopted in practical industrial applications and the current conveyors are being favored because of their enhanced characteristics. Thus, designing a PID controller using the current conveyors would augment its performance. Another unique aspect of the reported research work is the utilization of a crossover improved genetic algorithm for finding the controller’s transfer function. The controller has been optimally designed taking into consideration the unit step response characteristics. Subsequently, AD844 IC was used for practical implementation of the controller and the experimental observations are reported, which are found to be in good agreement with simulation as well as theoretical results.

     

病毒进化遗传算法的车辆调度优化模型

为了提高车辆调度优化效率,提出一种病毒进化遗传算法的车辆调度优化模型。建立车辆调度的数学模型,采用遗传算法对模型进行求解,并采用病毒群体感染主群体,主群体在历代个体间纵向传递信息以利于全局优化,病毒群体通过感染操作在同代个体间横向传递信息利于局部搜索,进行仿真对比实验。结果表明,病毒进化遗传算法较好地解决了标准遗传算法存在的不足,加快了车辆调度优化问题的求解效率,获得了更优的车辆调度方案,具有较高的应用价值。     

基于改进PatchMatch的自相似性图像超分辨率算法

针对基于自示例几何不变性的超分辨率算法没有充分利用图像的纹理和边缘信息,且采用的PatchMatch图像块匹配算法容易陷入局部极小值的问题,提出了一种基于改进PatchMatch的自相似性超分辨率算法。第一,利用模拟退火算法提高PatchMatch跳出局部极小点的能力;第二,定义边缘相似度因子并将其引入到图像块相似性的评价函数中;第三,受“局部自相似性”统计先验的启发,采用服从高斯概率分布的位置坐标搜索空间进行相似图像块匹配。实验表明,与当前先进算法相比,所提算法在视觉效果和客观评价指标上都有一定的提高。     

遗传算法优化多MPEG-7特征的检索算法

针对基于内容的图像检索难于通用化的问题,使用MPEG-7的颜色布局描述符和平行纹理描述符建立联合特征空间,并采用遗传算法对其内部特征权重以及描述符之间的权重进行优化。实验结果表明,使用上述方法大大降低了系统检索时间,明显提高检索效率。     

Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules

Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

决策树算法的程序演化方法优化

为了从另一个角度提高决策树算法的效率,对标准的ID3决策树算法进行函数式语言Haskell描述,研究了程序演化策略,推衍出了一个高效等价算法。实验结果证明演化过程是正确的。     

应用分段遗传算法的视频纹理合成算法

通过对视频纹理定义的分析,将视频纹理合成转化为一个优化组合问题。提出一种应用分段遗传算法的视频纹理合成算法,采用分段遗传算法,对有限长度的源视频进行加工,得到可无限播放的连续视频序列。算法采用更适当的相似性尺度和测量准则,省去了大量复杂的对源视频的预处理,分段的搜索策略只需要用很少的遗传代数即可快速合成出质量很高的视频纹理。与现有的视频纹理合成方法比较,该算法具有较小的计算复杂度,在合成的速度和质量上都有所提高。另外,实验结果给出了种群大小以及最大遗传代数对合成质量和速度的影响。     

改进遗传算法的并行任务调度

并行任务调度是一个NP完全问题,它关注资源的分配和并行任务调度,要求具有高性能的调度算法,且能求解出高质量的解。提出了一种基于改进遗传算法的并行任务调度算法,在算法初始化种群产生时引入任务向量矩阵来表示任务、资源以及调度的关系,并采用启发式方法得到初始化种群,提高种群质量;采用规则约束的交叉和变异操作,提高个体的质量;提出了加速进化策略,有效地避免了早熟。仿真实验结果表明,该改进算法能更有效地求解并行任务调度问题。     

基于遗传算法优化的跳汰机排料系统PID控制仿真

针对传统PID控制方法应用于跳汰机排料系统时难以获得最优控制参数、控制效果差的问题,提出一种基于遗传算法的PID控制参数优化方法,介绍了基于遗传算法优化的PID控制结构、参数优化方法及步骤,并以某矿井跳汰机排料系统为例,对基于该方法的PID控制器的控制性能进行了仿真研究。仿真结果表明,该方法能够实现PID控制参数的在线优化,收敛速度快,具有较强的鲁棒性;基于该方法的PID控制器具有良好的动、静态性能,无超调现象,控制精度高。     

改进遗传算法优化BP神经网络的语音情感识别

语音情感识别技术在人类生活中正扮演着越来越重要的作用。为了更为有效识别语音信号中的情感类型,提出一种改进遗传算法优化BP神经网络的识别算法（IAGA-BP）。该算法一方面改进了自适应遗传算法中的选择算子,另一方面更改了自适应遗传算法中的交叉和变异概率公式。通过对自适应遗传算法的改进,提升了遗传算法的寻优性能,并以此对BP神经网络初始的权阈值进行优化。在与BP、GA-BP和AGA-BP网络比较中,实验结果表明,IAGA-BP网络能够有效提高语音情感识别率,并加快了网路收敛速度。     

基于遗传算法优化的LS-SVM井下场强预测

为了进一步研究井下电波传播损耗规律,提高场强覆盖预测准确度,提出使用基于遗传算法优化的最小二乘支持向量机方法对井下巷道的场强进行预测。首先通过软件仿真生成巷道场强数据,并将数据分为训练集和测试集;然后采用最小二乘支持向量机方法对训练集进行学习,并使用遗传算法对最小二乘支持向量机方法的参数选择进行优化,采用测试集对方法性能进行验证;最后将基于遗传算法优化的最小二乘支持向量机方法用于井下巷道的场强预测。仿真实验结果表明,基于遗传算法优化的最小二乘支持向量机方法能够有效提高井下场强预测的精度,可获得较好的预测效果。     

基于改进自适应和声遗传算法的装配序列优化研究

在考虑装配工具及零件装配方向改变对装配成本影响的同时, 增加装配体约束稳定性影响作为装配序列优化评价要素, 构建装配成本模型。基于装配体几何约束关系, 建立装配方向约束矩阵和连接关系矩阵, 并依据专家知识得到零件所对应工具的映射表, 通过矩阵扫描得到装配成本惩罚次数, 并与惩罚系数加权得到装配成本函数值。在和声算法中引入自适应的参数调节方式及遗传算子, 使和声算法能够解决离散型数学问题, 并较好地改善了和声算法方向性差的缺点, 通过求解惩罚函数最小值, 得到其所对应的最优装配序列解, 最后结合实例对比, 验证了该方法有的效性。     

自适应GA优化WNN的模拟电路软故障诊断方法*

在小波神经网络(WNN)的模拟电路故障诊断系统中,普遍采用的梯度下降算法在训练时易使网络陷入局部最优,而网络结构的冗余也会造成训练收敛方向偏离全局最优点,降低推广能力和增加误诊率。用自适应遗传算法优化WNN,以克服上述缺陷。采用该方法可简化小波神经网络的结构和优化参数,在滤波器电路的软故障识别中获得满意的效果。与常规的WNN故障诊断方法相比,有效地提高了故障诊断的效率和正确率。     

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

Efficient vector quantization using genetic algorithm

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.     

Simulation optimisation using a genetic algorithm

The paper presents an attempt to apply genetic algorithms (GAs) to the problem of optimising an existing simulation model. A simple real-coded GA is presented and used to change the simulation model parameters. With each new parameter set proposed, a simulation run is performed. From the statistics gathered by running the simulation, a goal function is constructed that measures the quality of these parameters. Because of its nature and the stochastic and unpredictable behaviour of the complex simulation model, the goal function used leads to a highly non-linear, noisy and mixed (discrete and continuous) programming optimisation problem. A GA successfully works on it, and as a result gives a parameter set that measures substantially better than the nominal one. This demonstrates the capability of GAs to solve hard inverse problems even in the area of complex simulation model optimisation.