Description: 此程序代码为基于遗传算法的PID整定，利用MATLAB编程实现。该程序通过遗传算法实现参数寻优，是一种不需要任何初始信息并可以寻求全局最优解的、高效的优化组合方法。-this procedure code based on genetic algorithms for PID tuning, using MATLAB programming. The procedures through genetic algorithm optimization of the parameters. is a not need any initial information and can find the global optimum, efficient optimization method. Platform: |
Size: 1492 |
Author:周龙 |
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Description: 本程序为基于工艺参数优化的改进遗传算法程序-based on the procedures for the optimization of process parameters improved genetic algorithm Platform: |
Size: 202822 |
Author:徐文臣 |
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Description: 此程序代码为基于遗传算法的PID整定，利用MATLAB编程实现。该程序通过遗传算法实现参数寻优，是一种不需要任何初始信息并可以寻求全局最优解的、高效的优化组合方法。-this procedure code based on genetic algorithms for PID tuning, using MATLAB programming. The procedures through genetic algorithm optimization of the parameters. is a not need any initial information and can find the global optimum, efficient optimization method. Platform: |
Size: 1024 |
Author:周龙 |
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Description: 将遗传算法(GA)与传统SVM算法结合,构造出一种参数最优的进化SVM(GA2SVM),SVM 模型采用径向基函数(RBF)作为核函数，利用格雷码编码方式对SVM算法的模型参数进行遗传编码和优化搜索，将搜索到的优化结果作为SVM 的最终模型参数。-Genetic algorithm (GA) combined with the traditional SVM algorithm, a kind of tectonic evolution of the optimal parameters of SVM (GA2SVM), SVM model using Radial Basis Function (RBF) as kernel function, the use of Gray code encoding algorithm of the SVM model parameters of genetic coding and optimization of search, will search for the optimal results as the final SVM model parameters. Platform: |
Size: 179200 |
Author:zhaoxiufen |
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Description: 利用遗传算法寻优。待寻优函数为y=xx,参数变化范围为0-31。-The use of genetic algorithm optimization. Optimization function to be y = xx, the parameters for the 0-31 range. Platform: |
Size: 1024 |
Author:崔艳 |
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Description: 用Java实现的进化算法包。包括遗传算法、粒子群算法、memetic算法和进化策略算法。-evolutionary-algorithm
Evolutionary Algorithm package implemented using Java.
The package serves as a foundation class library, supporting the implementation many variants of Evolutionary Algorithms, currently including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithm (MA), Evolution Strategy (ES).
Highlighted features
· Support both binary & real-coded string representations of solution
· Operator-based design for flexibility
· EA Operators: Selection, Crossover, Mutation, Move operators in PSO & and the adaptive scheme in EA
· Individual learning: Davidon–Fletcher–Powell (DFP) and Davies, Swann, and Campey with Gram-Schmidt orthogonalization (DSCG) strategies and Random Mutation Hill-climbing (RMHC)
In addition, algorithm pipeline which is specified by XML file is also provided for practitioner to configure & design evolutionary algorithms at ease. User can edit runtime & algorithm parameters in the configuration file (XML) & issue the co Platform: |
Size: 104448 |
Author:陈雷 |
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Description: 这是用于电火花机床加工控制参数优化的一个多目标遗传算法的matlab程序，自己原创，分享出来和大家交流。 -It is used for electric discharge machining control parameters optimization of a multi-objective genetic algorithm matlab program, their own originality, share them and everyone exchanges. Platform: |
Size: 2048 |
Author:袁计委 |
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Description: 在实时平台上,高斯混合模型(GMM)具有计算有效性和易于实现的优点。最大似然规则中,模型参数不
断更新,但由于爬山特征,任意的原始模型参数估计通常将导致局部最优 遗传算法(GA)适于求解复杂组合优化问
题及非线性函数优化。提出了基于说话人识别的可以解决GMM局部最优问题的GMM/GA新算法,实验结果表明,
提出的GMM/GA新算法比纯粹的GMM算法能获得更优的效果。
- In real-time platform, the Gaussian mixture model (GMM) with the calculation of the effectiveness and easy to realize benefits. Maximum likelihood rule, the model parameters are not
Broken updates, but due to climbing features, any of the original model parameter estimation will usually result in local optimum genetic algorithm (GA) is suitable for solving complex combinatorial optimization question
Title and non-linear function optimization. Proposed speaker recognition based on GMM can solve the problem of local optimal GMM/GA new algorithm, experimental results show that the
Proposed GMM/GA new algorithm than purely GMM algorithm can get better results. Platform: |
Size: 4448256 |
Author:于高 |
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Description: Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernel
parameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature selection is another factor that impacts
classifi cation accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM
classifi cation accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. Platform: |
Size: 141312 |
Author:payal |
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Description: tinyos 蚁群算法(ant colony optimization, ACO)，又称蚂蚁算法，是一种用来在图中寻找优化路径的机率型算法。它由Marco Dorigo于1992年在他的博士论文中提出，其灵感来源于蚂蚁在寻找食物过程中发现路径的行为。蚁群算法是一种模拟进化算法,初步的研究表明该算法具有许多优良的性质.针对PID控制器参数优化设计问题,将蚁群算法设计的结果与遗传算法设计的结果进行了比较,数值仿真结果表明,蚁群算法具有一种新的模拟进化优化方法的有效性和应用价值-The tinyos ant colony algorithm (ant colony optimization, ACO), also known as ant algorithm the the probability type algorithm is a method for finding the optimal path in the graph. By Marco Dorigo in his doctoral thesis in 1992, inspired by the behavior of ants found in the process of looking for food path. Ant colony algorithm is a simulated evolutionary algorithm, preliminary studies show that the algorithm has many excellent properties for the PID controller parameters to optimize the design problem, the design of ant colony algorithm and genetic algorithm design compared with numerical simulation results The results show that the ant colony algorithm with a new simulated evolutionary optimization method effectiveness and value Platform: |
Size: 495616 |
Author:Sofunzhao |
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Description: 基于量子遗传算法的多目标优化，参数为种群规模m二50，量子位数n二2，转角步长初值氏=0.01二，变异概率p二二0.1，交叉概率Pc二0.8，限定代数丈~二5-Quantum genetic algorithm-based multi-objective optimization parameters for population size m 50, the quantum number n = 2, the corner step initial value s = 0.01, mutation probability p = 0.1, crossover probability Pc 0.8, limited-generation Shu Zhang ~ Second 500 Platform: |
Size: 1024 |
Author:lihaibin |
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Description: 本文主要介绍遗传算法的基本理论，叙述遗传算法在图像增强的的主要应用，即将原始图像变得更加清晰，特征变得更加明显。
现今关于图像增强的算法有很多，而这些算法大多是基于退化函数或者点扩展函数的知识进行图像处理的。当图像出现模糊或噪声影响大时，设计出的图像清晰化的效果肯定不够理想，因此有必要对图像进行增强处理。于是，可利用到遗传算法这种成熟稳定的仿生物进化的全局寻优算法，进行图像增强，由于遗传算法控制参数少、自适应度高，将选择该方法对图像退化分别进行不同的清晰化处理. 这样既增强了图像的对比度,又克服了传统直方图均衡化处理所造成的灰度级损失等缺点。
通过实验去表明，遗传算法从全局寻优的角度出发，能够较为精确地估计退化系统函数。和传统的线性增强、直方图均衡进行比较。实验结果表明该方法能改善原图像视觉效果，便于之后的图像分析。
-This paper mainly introduces the basic theory of genetic algorithm, genetic algorithm (ga) in the main application of image enhancement, the original image is more clear, characteristic becomes more obvious.
Today about the image enhancement algorithm are many, and most of these algorithms is based on the degradation function or the knowledge of the point spread function of image processing. When the image appears fuzzy or noise influence, designed the image clarity of effect must not ideal, therefore it is necessary to enhance the image. Then, genetic algorithm is available to the mature and stable evolution of the global optimization algorithm, for image enhancement, due to less genetic algorithm control parameters, since the high fitness, will choose the method of image degradation, respectively, to manipulate the different motivation. It can enhance the image contrast, and overcome the traditional histogram equalization processing grayscale loss caused by the faults.
Throug Platform: |
Size: 1670144 |
Author:古志榮 |
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Description: Power system stabilizers (PSS) are now routinely used
in the industry to damp out power system oscillations. In this paper,
real-coded genetic algorithm (RCGA) optimization technique is
applied to design robust power system stabilizer for both singlemachine
infinite-bus (SMIB) and multi-machine power system. The
design problem of the proposed controller is formulated as an
optimization problem and RCGA is employed to search for optimal
controller parameters. By minimizing the time-domain based
objective function, in which the deviation in the oscillatory rotor
speed of the generator is involved stability performance of the
system is improved. The non-linear simulation results are presented
under wide range of operating conditions disturbances at different
locations as well as for various fault clearing sequences to show the
effectiveness and robustness of the proposed controller and their
ability to provide efficient damping of low frequency oscillations.-Power system stabilizers (PSS) are now routinely used
in the industry to damp out power system oscillations. In this paper,
real-coded genetic algorithm (RCGA) optimization technique is
applied to design robust power system stabilizer for both singlemachine
infinite-bus (SMIB) and multi-machine power system. The
design problem of the proposed controller is formulated as an
optimization problem and RCGA is employed to search for optimal
controller parameters. By minimizing the time-domain based
objective function, in which the deviation in the oscillatory rotor
speed of the generator is involved stability performance of the
system is improved. The non-linear simulation results are presented
under wide range of operating conditions disturbances at different
locations as well as for various fault clearing sequences to show the
effectiveness and robustness of the proposed controller and their
ability to provide efficient damping of low frequency oscillations. Platform: |
Size: 470016 |
Author:ali |
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Description: n this paper, Optimization is considered as the main impact of insight problem and heuristic methods. A proposed method is represented by using two optimization algorithms cuckoo optimization is heuristic method and Genetic algorithm is meta-heuristic method in order to increase the optimization level and speed of calculation as possible. The proposed methodology and technique still a subject for improvements and enhancements for increasing its speeding up more and more. The proposed method is applicable for many industrial fields, agricultural fields and other provided that having optimization problem just reconfigure problem parameters and have good opportunity to optimize well the problem.-n this paper, Optimization is considered as the main impact of insight problem and heuristic methods. A proposed method is represented by using two optimization algorithms cuckoo optimization is heuristic method and Genetic algorithm is meta-heuristic method in order to increase the optimization level and speed of calculation as possible. The proposed methodology and technique still a subject for improvements and enhancements for increasing its speeding up more and more. The proposed method is applicable for many industrial fields, agricultural fields and other provided that having optimization problem just reconfigure problem parameters and have good opportunity to optimize well the problem. Platform: |
Size: 587776 |
Author:Mohamed El-dosuky |
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Description: 遗传算法提供了求解非线性规划的通用框架，它不依赖于问题的具体领域。遗传算法的优点是将问题参数编码成染色体后进行优化， 而不针对参数本身， 从而不受函数约束条件的限制； 搜索过程从问题解的一个集合开始， 而不是单个个体， 具有隐含并行搜索特性， 可大大减少陷入局部最小的可能性。而且优化计算时算法不依赖于梯度信息，且不要求目标函数连续及可导，使其适于求解传统搜索方法难以解决的大规模、非线性组合优化问题。(Genetic algorithm provides a general framework for solving nonlinear programming, which does not depend on the specific problem domain. The advantage of genetic algorithm is that the problem parameters are encoded into chromosomes for optimization, rather than the parameters themselves. The search process starts from a set of problem solutions, rather than a single individual, and has the implicit parallel search feature, which can greatly reduce the possibility of falling into the local minimum. Moreover, the algorithm does not rely on gradient information and does not require the objective function to be continuous and differentiable, which makes it suitable for solving large-scale and nonlinear combinatorial optimization problems that are difficult to be solved by traditional search methods.) Platform: |
Size: 33792 |
Author:FZenjoys |
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Search - optimization parameters for Genetic Algorithm