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: 本程序用来实现对非线性函数求解优化程序的主要算法采用遗传算法实现-This procedure is used to achieve non-linear functions to solve optimization procedure using genetic algorithm main algorithms Platform: |
Size: 2048 |
Author:罗杰 |
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Description: This an implementation of Particle Swarm Optimization algorithm using
the same syntax as the Genetic Algorithm Toolbox, with some additional
options specific to PSO. Allows code-reusability when trying different
population-based optimization algorithms. Certain GA-specific parameters
such as cross-over and mutation functions will not be applicable to the
PSO algorithm. Demo function included, with a small library of test functions. Requires Optimization Toolbox.-This is an implementation of Particle Swarm Optimization algorithm using
the same syntax as the Genetic Algorithm Toolbox, with some additional
options specific to PSO. Allows code-reusability when trying different
population-based optimization algorithms. Certain GA-specific parameters
such as cross-over and mutation functions will not be applicable to the
PSO algorithm. Demo function included, with a small library of test functions. Requires Optimization Toolbox. Platform: |
Size: 4096 |
Author:Chris Leung |
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Description: 使用基本遗传算法（GA）对函数进进行最优化的C语言源程序可直接使用。
-Using the basic genetic algorithm (GA) optimization functions into C language source code can be used directly. Platform: |
Size: 8192 |
Author:bargain |
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Description: 3元简单函数优化的遗传算法程序,利用遗传算法求三元函数的最优值，包括主程序、初始化、交叉、变异、选择等子程序，方便自己修改，并且包含图像分析。-A simple function of the genetic algorithm optimization procedures, the optimal value of ternary functions using genetic algorithms, including the subroutine of the main program, initialization, crossover and mutation, selection, convenient modify, and includes image analysis. Platform: |
Size: 4096 |
Author:chennan |
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Description: 设每位同学自己的学号为n，采用遗传算法求解下面优化问题：
min f(x1,x2)=(4-2.1*x1^2+(x1^4)/3)*x1^2+x1*x2+(-4+4*x2^2)*x2^2+n-20111369
s.t. -3<x1<3 -2<x2<2
函数 为六峰值驼背函数（Six-hump Camel Back Function），要求如下：
（i）mesh()为Matlab中常用的画图函数，请查阅相关书籍或help，掌握mesh用法，画出 的三维图像；
（ii）用遗传算法工具箱求解该优化问题，给出求解结果，画出每代群体的最优适应度、适应度均值图像。
（iii）尝试至少一种提高求解效果的方法，并简单比较、分析。
-Set each student their own student number for n, using the genetic algorithm for solving the following optimization problem:
Min (x1, x2) = f (4-2.1* x1 ^ 2+ (x1 ^ 4)/(3)* x1 ^ 2* x2+ x1+ x2 ^ (4+ 4* 2)* x2 ^ 2+ n- 20111369
S.t.- 3 < x1 < 3-2 "x2" 2
Functions for Six peak hunchback (Six- hump Camel Back Function), the requirements are as follows:
(I) mesh () as that is commonly used in Matlab drawing function, or help please refer to the relevant books, to master the use mesh, draw the 3 d image
(ii) using genetic algorithm toolbox to solve the optimization problem, is given as a result, to draw the best fitness of each generation group, the mean fitness images.
(iii) to try at least a way to improve the effect of solution, and a simple comparison and analysis.
Platform: |
Size: 1024 |
Author:天溟 |
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Description: 利用Matlab来对函数进行优化，遗传算法具有较好的优化性能，针对一些简单函数，用遗传算法进行优化设计程序-To optimize the use of Matlab function, genetic algorithm has better optimize performance for some simple functions, using genetic algorithms to optimize the design process. . . . Platform: |
Size: 4096 |
Author:张龙 |
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Search - optimization functions using genetic algorithm