遗传算法采用进化方向算子的几种策略

提出了采用实数编码情况下应用进化方向算子的几种策略,包括单亲进化方向算子、双亲进化方向算子以及无轮盘赌选择的双亲进化方向算子策略,并进行了数值仿真。仿真结果表明,灵活使用方向进化算子以及遗传操作可大大提高遗传算法的全局搜索能力。     

A genetic algorithm approach to the spectral estimation of time series with noise and missed observations

This study considers the problem of estimating the autoregressive moving average (ARMA) power spectral density when measurements are corrupted by noise and by missed observations. The missed observations model is based on a probabilistic structure. Unlike conventional cases of missed observation in parameter estimation problems, the variance of noise is unavailable, that is the time points of missed observations are unknown, and the probability of missing data needs to be estimated. In this situation, spectral estimation is more difficult to solve and becomes a highly nonlinear optimization problem with many local minima. In this paper, we use the genetic algorithm (GA) method to achieve a global optimal solution with a fast convergence rate for this spectral estimation problem. From the simulation results, we have determined that the performance is significantly improved if the probability of data loss is considered in the spectral estimation problem.     

Analysis and application of a novel fast algorithm for 2-D ARMA model parameter estimation

In this paper, we analyze a novel algorithm for 2-D ARMA model parameter estimation in the presence of noise and then develop a fast and efficient blind image restoration algorithm. We show that the novel algorithm can minimize a quadratic convex optimization problem and has a lower computational complexity than the conventional algorithms. As a result, the novel algorithm involves no convergence and local minimum issue. Moreover, the proposed blind image restoration algorithm can overcome the local minimization problem. Computed results confirm that the novel algorithm can more quickly obtain more accurate estimates than the conventional algorithms in the presence of noise.     

Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach

The non-parametric k-nearest neighbour (k-NN) multi-source estimation method is commonly employed in forest inventories that use satellite images and field data. The method presumes the selection of a few estimation parameters. An important decision is the choice of the pixel-dependent geographical area from which the nearest field plots in the spectral space for each pixel are selected, the problem being that one spectral vector may correspond to several different ground data vectors. The weighting of different spectral components is an obvious problem when defining the distance metric in the spectral space.The paper presents a new method. The first innovation is that the large-scale variation of forest variables is used as ancillary data that are added to the variables of the multi-source k-NN estimation. These data are assigned weights in a way similar to the spectral information of satellite images when defining the applied distance metric. The second innovation is that “optimal” weights for spectral data, as well as ancillary data, are computed by means of a genetic algorithm. Tests with practical forest inventory data show that the method performs noticeably better than other applications of k-NN estimation methods in forest inventories, and that the problem of biases in the species volume predictions can for example, almost completely be overcome with this new approach.     

应用遗传算法估算溶液热力学模型参数

应用遗传算法估算溶液热力学模型参数,并对标准遗传算法中的变异策略和竞争方式作了适当的改进,得到改进的遗传算法。举DMF(二甲基甲酰胺)+water体系的溶液热力学模型参数的估算为例,并与POWELL法比较。计算结果表明,遗传算法比POWELL法具有更强的寻优能力,而本文所提出的改进的遗传算法比标准遗传算法的寻优速度明显较快,对解决溶液热力学模型这类复杂的非线性函数的参数估算问题,本文所提出的改进的遗传算法是一种较好的寻优算法。     

Can the Sun's Direction be Estimated from an Image Prior to the Computation of Object Shape?

Various computational techniques have been developed that performreasonably well in inferring shape from shading. However, thesetechniques typically require substantial prerequisite information ifthey are to evolve an estimate of surface shape. It is thereforeinteresting to consider how depth might be inferred from shadinginformation without prior knowledge of various scene conditions. Oneapproach has been to undertake a pre-processing step ofestimating the light-source direction, thereby providing input tothe computation of shape from shading. In this paper, we presentevidence that a versatile light-source-direction estimator isunattainable, and propose that, in the absence of domain-specificknowledge, shape and light-source direction should be determined ina coupled manner     

Stochastic averaging analysis of a steepest-descent-type adaptive time-delay estimation algorithm

In this paper stochastic averaging analysis tools are used to study an adaptive time-delay estimation algorithm. Analyzing such an algorithm is very difficult because of its nonlinear, infinite-dimensional, and time-variant nature. By stochastic averaging analysis, we show that for the time-invariant delay case, the adaptive algorithm output converges weakly to the solution of an ordinary differential equation. Local convergence is demonstrated by showing that the solution of this differential equation converges exponentially to the true delay under reasonable initial conditions. Implementation of the algorithm is also discussed. Guided by the averaging results, a modified algorithm is proposed to eliminate the bias of the delay estimation. Second-order analysis is carried out and the results provide a theoretical justification of the observations made by other researchers with simulation and heuristic argument. Computer simulations are also included to support the analysis.     

Genetic algorithm to solve the p-centre and p-radius problem on a network

The p-centre problem is to locate p facilities on a network so as to minimize the largest distance from a demand point to its nearest facility. The problem is NP-complete for an arbitrary network. In this paper, genetic algorithms (GAs) to solve this problem are developed via two different representations. The nodes are taken as weighted, and the demand points are assumed to coincide with the nodes. Computational results obtained from the proposed GAs for different network sizes and different values of p are presented and compared for two different representations.     

Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis

This paper presents an evolving ant direction particle swarm optimization algorithm for solving the optimal power flow problem with non-smooth and non-convex generator cost characteristics. In this method, ant colony search is used to find a suitable velocity updating operator for particle swarm optimization and the ant colony parameters are evolved using genetic algorithm approach. To update the velocities for particle swarm optimization, five velocity updating operators are used in this method. The power flow problem is solved by the Newton–Raphson method. The feasibility of the proposed method was tested on IEEE 30-bus, IEEE 39-bus and IEEE-57 bus systems with three different objective functions. Several cases were investigated to test and validate the effectiveness of the proposed method in finding the optimal solution. Simulation results prove that the proposed method provides better results compared to classical particle swarm optimization and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.     

A neural network construction algorithm with application to image compression

We propose an algorithm for constructing a feedforward neural network with a single hidden layer. This algorithm is applied to image compression and it is shown to give satisfactory results. The neural network construction algorithm begins with a simple network topology containing a single unit in the hidden layer. An optimal set of weights for this network is obtained by applying a variant of the quasi-Newton method for unconstrained optimisation. If this set of weights does not give a network with the desired accuracy then one more unit is added to the hidden layer and the network is retrained. This process is repeated until the desired network is obtained. We show that each addition of the hidden unit to the network is guaranteed to increase the signal to noise ratio of the compressed image.     

基于块运动类型与阈值相结合的运动估计算法

现有视频压缩法中大多都能够得到较好的压缩视频的效果,但其算法的运算过程往往存在着运算量大、复杂度高等问题.根据UMHexagonS算法的准确性与高效性,采用H.264 UMHexagonS快速运动估计算法,通过在其运算过程中加入阈值并且结合视频运动类型的自适应法,有效降低了搜索点数,提高了运动估计算法效率.通过在JM测试平台上进行了算法验证,验证了该算法能在保证编码性能的同时,快速、有效的减少算法的运算量与时间.与预期的结果相符,在无线视频监控领域中具有较好的发展前景.     

Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design

The application of evolutionary algorithms (EAs) is becoming widespread in engineering optimisation problems because of their simplicity and effectiveness. The Alopex-based evolutionary algorithm (AEA) possesses the basic characteristics of heuristic search algorithms but is lacking in adequate information about the fitness landscape of the input domain, reducing the convergence speed. To improve the performance of AEA, the Gaussian copula estimation of distribution algorithm (EDA) is embedded into the original AEA in this paper. With the help of Gaussian copula EDA, precise probability models are built utilising the best solutions, which can increase the convergence speed, and at the same time, keep the population diversity as much as possible. The simulation results on the benchmark functions and the application to the Butterworth filter design demonstrate the efficiency and effectiveness of the proposed algorithm, compared with several other EAs.     

基于FPGA的遗传算法的实现

遗传算法是基于自然选择法则的一种鲁棒并行计算方法,它可广泛应用于各类分布式与集中式的工业优化控制过程中。遗传算法本质的并行性及其操作的简单性使其非常适合用现场可编程逻辑器ＦＰＧＡ实现。提出了一种基于ＦＰＧＡ的遗传算法的硬件系统,实现了高效并行计算平台。     

Genetic algorithm to maximize a lower-bound for system time-to-failure with uncertain component Weibull parameters

A genetic algorithm (GA) is used to solve the redundancy allocation problem when the objective is to maximize a lower percentile of the system time-to-failure distribution and the available components have random Weibull scale parameters. The GA searches the prospective solution space using an adaptive penalty to consider both feasible and infeasible solutions until converging to a feasible recommended system design. The objective function is intractable and a bi-section search is required as a function evaluator. Previously, this problem has most often been formulated to maximize system reliability instead of a lower-bound on system time-to-failure. Most system designers and users are risk-averse, and maximization of a lower percentile of the system time-to-failure distribution is a more conservative strategy (i.e. less risky) compared to maximization of the mean or median of the time-to-failure distribution. The only previous research to consider a lower percentile of system time-to-failure, also required that all component Weibull parameters are known. Those findings have been extended to address problems where the Weibull shape parameter is known, or can be accurately estimated, but the scale parameter is a random variable. Results from over 90 examples indicate that the preferred system design is sensitive to the user's perceived risk.     

A fuzzy GARCH model applied to stock market scenario using a genetic algorithm

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model.The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.     

Development of a geometric modelling strategy for roll pass optimal design

Roll pass design is one of the most important tasks in shape rolling operations that are employed to provide raw materials with appropriate cross-section profiles for various industrial applications. Currently, many approaches, such as experience-based trial-and-error strategies, finite element methods, and expert systems, are applied to improve both quality and efficiency of roll pass design. However, due to lack of a flexible geometrical modelling strategy, the application of extant approaches is largely limited. This study attempts to develop a novel approach for generic geometrical modelling to support optimal design of roll passes. Features of the proposed model are analysed to support its application. Furthermore, a parameters estimation approach based on genetic algorithm is also developed to facilitate the transformation between the generic model and other geometrical models, as well as to improve its flexibility and applicability. The results from the case study presented in the paper indicate that the new model is more flexible and efficient, and that the parameters estimation approach also can achieve high transformation accuracy and efficiency.     

遗传算法在多目标优化应用中的对比研究

多目标优化应用研究在过程工程领域越来越受重视。本文首先给出了多目标优化问题的一般形式,指出多目标问题求解任务：引导搜索向整个的Pareto优化范围;Pareto优化前沿上保持解集的多样性。在简要论述遗传算法求解多目标技术的基础上,对应用了遗传算法求解多目标的两种方法进行了对比研究,并给出了线性加权遗传算法和一种多目标遗传算法的计算框图。指出线性加权法求解Pareto最优解时不能不能很好地处理非凸区域、均匀分布的权重值不能生成均匀分布的Pareto前沿等局限性,以及多目标遗传算法生成种群多样性及Pareto最优解均匀分布的优点,并用实例进行了验证说明。     

A novel classification method: A hybrid approach based on extension of the UTADIS with polynomial and PSO-GA algorithm

In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used as the utility function rather than using a piecewise linear function as an approximation of the utility function of each attribute. We called this method as PUTADIS. The objective is calculating the coefficients of the polynomial and the threshold limit of classes and weight of attributes such that it minimizes the number of misclassification error. Estimation of unknown parameters of the problem is calculated by using a hybrid algorithm which is a combination of particle swarm optimization algorithm (PSO) and Genetic Algorithm (GA). The results obtained by implementing the model on different datasets and comparing its performance with other previous methods show the high efficiency of the proposed method.     

基于遗传算法的RBF神经网络的优化设计方法

该文提出了一种新的RBF神经网络的设计方法,采用遗传算法对RBF神经网络的隐层节点中心值进行进化优选,用自适应梯度下降法选择隐层节点高斯函数的宽度,用递推的最小二乘法训练RBF神经网络的权值,仿真结果证明了该方法的有效性。