Description: 遗传算法解决双变量的函数最优化问题，有按钮的界面，用bc所编，生动模拟遗传进化过程-genetic algorithm to solve the two- variable optimization function, the button interface, using bc prepared by the vivid simulation of the process of genetic evolution Platform: |
Size: 5120 |
Author:连宙辉 |
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Description: 本文采用C++开发遗传算法，并由次算法解决最短路算法，函数最优化算法，取得了良好的效果。-this paper, C development of genetic algorithms, meeting with the shortest path algorithm for solving algorithms, function optimization algorithm, achieved good results. Platform: |
Size: 477184 |
Author:高路新 |
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Description: 基于遗传算法和神经网络的黑箱函数优化举例matlab程序-based on genetic algorithms and neural network as a black box function optimization procedures for Matlab Platform: |
Size: 2048 |
Author:张鹏 |
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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: 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: 简单一元函数优化实例，利用遗传算法计算函数f(x)=x*sin(10pi*x)+2.0,-1<=x<=2的最大值-One dollar a simple example of function optimization using genetic algorithm function f (x) = x* sin (10pi* x)+ 2.0,-1 <= x <= 2 the maximum value of Platform: |
Size: 1024 |
Author:杜勇 |
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Description: yj1.m：简单一元函数优化实例，利用遗传算法计算下面函数的最大值
包括了遗传算法的12个经典例题，适合大家学习参考。-yj1.m: a dollar a simple example of function optimization using genetic algorithm function below the maximum value of the genetic algorithm including 12 classic example for everyone to learn the reference. Platform: |
Size: 46080 |
Author:肖明 |
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Description: 简单一元函数优化实例，利用遗传算法计算下面函数的最大值-One dollar a simple example of function optimization using genetic algorithm function below the maximum Platform: |
Size: 1024 |
Author:齐乐 |
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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: 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: |
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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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Search - function optimization genetic