Description: 遗传算法包括基本算法的程序及其在优化函数方面的应用-genetic algorithms include basic algorithm for the optimization process and its function in the application Platform: |
Size: 10240 |
Author:朱易 |
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Description: ：介绍遣传算法的基本原理和Matlab的遗传算法优化工具箱(GAOT)，分析了优化工具函数。探讨Matlab遗传算法工具箱在
参数优化和非线性规划中的应用。通过优化实例，说明遗传算法是一种具有良好的全局寻优性能的优化方法。用Maflab语
言及Maflab语言编制的优化工具箱进行优化设计具有语言简单、函数丰富、用法比较灵活、编程效率高等特点。-: Removal algorithm introduce the basic principles and Matlab Genetic Algorithm Optimization Toolbox (GAOT), an analysis of function optimization tool. Explore the Matlab Genetic Algorithm Toolbox in the parameter optimization and nonlinear programming applications. Through optimization examples to illustrate the genetic algorithm is a good global optimization method to optimize performance. Maflab language and using language Maflab Optimization Toolbox to optimize the design of the language is simple, function-rich, and using more flexible programming and high efficiency. Platform: |
Size: 210944 |
Author:icyrock |
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Description: 实数编码遗传算法求函数极大值，实现求Rosenbrock函数极大值的优化计算的实数编码遗传算法-Real-coded genetic algorithm for maximum function, Rosenbrock function realize maximum value for the optimization calculation of real-coded genetic algorithm Platform: |
Size: 1024 |
Author:纷纷 |
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Description: 二进制编码遗传算法求函数极大值，实现Rosenbrock函数极大值优化计算的二进制编码遗传算法
-Binary-coded genetic algorithm for maximum function, Rosenbrock function to achieve maximum optimization of the calculation of the binary-coded genetic algorithm Platform: |
Size: 1024 |
Author:纷纷 |
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Description: 用MA TLAB 语言及MA TLAB 语言编制的优化工具箱进行优化设计具有语言简单、函数丰富、用法比
较灵活、编程效率高等特点. 本文对遗传算法和基于MA TLAB 的遗传算法优化工具箱(GAO T ) 作了简要的介
绍、分析了优化工具函数, 并结合非线性、多峰值函数问题的优化实例, 说明了遗传算法是一种具有良好的全局
寻优性能的优化方法.-MA TLAB with language and language of the MA TLAB optimization toolbox to optimize the design of the language is simple, function-rich, and using more flexible programming and high efficiency. In this paper, genetic algorithm and MA TLAB based on genetic algorithm optimization toolbox (GAO T ) gave a brief introduction, an analysis of function optimization tool, combined with non-linear, multimodal function optimization problem examples to illustrate the genetic algorithm is a good global optimization method to optimize performance. Platform: |
Size: 269312 |
Author:dh |
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Description: 简单一元函数优化实例，利用遗传算法计算函数的最大值-One dollar a simple example of function optimization, genetic algorithm using the maximum function Platform: |
Size: 636928 |
Author:daniel |
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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: 本算法是一种标准遗传算法，应用于函数优化，性能较好。-The algorithm is a standard genetic algorithm, applied to function optimization, performance better. Platform: |
Size: 1024 |
Author:wuhao3424 |
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Description: 简单函数优化的遗传算法程序 简单函数优化的遗传算法程序-Simple function of the genetic algorithm optimization process simple function of the genetic algorithm optimization process simple function of the genetic algorithm optimization procedure Platform: |
Size: 4096 |
Author:jianhuajuly |
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Description: 利用matlab编写的一些简单函数优化的遗传算法程序～-Matlab prepared to use some simple function of the genetic algorithm optimization process ~ Platform: |
Size: 4096 |
Author:何洪举 |
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Description: 这是改进的遗传算法，在函数优化中，比简单的遗传算法能更好的收敛且精确度更高！-This is the improved genetic algorithms, function optimization in more than a simple genetic algorithm to better convergence and higher accuracy! Platform: |
Size: 1024 |
Author:陈世界 |
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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: 用BP网络建立映射关系，为遗传算法提供适应度函数，通过改进遗传算法完成最小值优化-the mapped relation is built using BP network in order to provide fitness function for genetic algorithm. At last, optimization of minimum value is finished by genetic algorithm. Platform: |
Size: 1024 |
Author:wangchanglong |
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Description: This a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those found in the Optimization Toolbox, are
fast and accurate for many types of optimization problems. These methods
are designed to solve smooth , i.e., continuous and differentiable,
minimization problems, as they use derivatives to determine the direction
of descent. While using derivatives makes these methods fast and
accurate, they often are not effective when problems lack smoothness,
e.g., problems with discontinuous, non-differentiable, or stochastic
objective functions. When faced with solving such non-smooth problems,
methods like the genetic algorithm or the more recently developed pattern
search methods, both found in the Genetic Algorithm and Direct Search
Toolbox, are effective alternatives. -This is a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those found in the Optimization Toolbox, are
fast and accurate for many types of optimization problems. These methods
are designed to solve smooth , i.e., continuous and differentiable,
minimization problems, as they use derivatives to determine the direction
of descent. While using derivatives makes these methods fast and
accurate, they often are not effective when problems lack smoothness,
e.g., problems with discontinuous, non-differentiable, or stochastic
objective functions. When faced with solving such non-smooth problems,
methods like the genetic algorithm or the more recently developed pattern
search methods, both found in the Genetic Algorithm and Direct Search
Toolbox, are effective alternatives. Platform: |
Size: 18432 |
Author:gao |
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Description: 遗传算法
求下列函数的最大值
obj(x)=x.*(10-x).*sin(2*pi*x), x\in[0,10].-Optimization problem by genetic algorithm of a function
obj(x)=x.*(10-x).*sin(2*pi*x), x\in[0,10]. Platform: |
Size: 5120 |
Author:HU |
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Description: 基因遗传算法中的一元函数的优化举例，供广大初学之人黏贴使用-Genetic algorithm optimization of unary function example, for the majority of beginners person adhesive used Platform: |
Size: 1024 |
Author:Baixianxu |
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Search - optimization of function by genetic algorithm