Description: 这是遗传算法进行函数优化的程序，可以运行成功，需要的就下吧-This is the source code of genetic algorithm for function optimization, it can run ,down it if you need it. Platform: |
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Author:wlr |
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Description: 基于基本遗传算法的函数最优化SGA.C
A Function Optimizer using Simple Genetic Algorithm developed from the Pascal SGA code presented by David E.Goldberg-based on the basic genetic algorithm optimization function SGA.C A Optimize Function r using Simple Genetic Algorithm developed fro m Pascal SGA the code presented by David E. Goldb erg Platform: |
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Author:J.Chen |
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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: |
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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: 常用matlab遗传算法目标函数的编写实例，如：双积分优化，收获系统，装载系统等-Matlab genetic algorithm commonly used objective function of the preparation of examples, such as: double-integrating optimization, harvest systems, loading systems, etc. Platform: |
Size: 2048 |
Author:熊梅西 |
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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: 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: 用遗传算法优化神经网络权值 最后实现逼近sin函数曲线-Neural network using genetic algorithm optimization to achieve the right of the value of the final function curve approximation sin Platform: |
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Author:无名 |
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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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Description: Matlab 遗传算法(Genetic Algorithm)优化工具箱是基于基本操作及终止条件、二进制和十进制相互转换等操作的综合函数库。其实现步骤包括：通过输入及输出函数求出遗传算法主函数、初始种群的生成函数，采用选择、交叉、变异操作求得基本遗传操作函数。以函数仿真为例，对该函数优化和GA 改进，只需改写函数m 文件形式即可。(The Matlab Genetic Algorithm optimization toolbox is a comprehensive function library based on basic operations and termination conditions, binary and decimal conversion and other operations. The implementation steps include: the main function of genetic algorithm and the generation function of the initial population are obtained by the input and output functions, and the basic genetic operation function is obtained by the selection, crossover and mutation operations. Taking function simulation as an example, the function optimization and GA improvement only need to rewrite function m file form) Platform: |
Size: 9216 |
Author:FZenjoys |
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Search - genetic algorithm optimization function