Description: 改进的GA.
fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!
-improved GA. Fga.m for the main program of genetic algorithm using binary Gray code, Based on the roulette method of nonlinear ranking choice, uniform crossover and mutation, but also to introduce the inversion operation. Platform: |
Size: 3072 |
Author:林洋 |
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Description: GA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。
本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值啦）
并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；-GA (Simple Genetic Algorithm) is a powerful, intelligent multi-variable optimization algorithms, which mimic the breeding populations of the law to be optimized. SGA can optimize this variable, and the minimum value, maximum value (when the function of the countdown you will seek the minimum value) and to support the floating-point encoding, grey code, binary code round of gambling options, tournament selection single-point crossover, uniform Cross, floating-point crossover single point mutation, floating-point variation Platform: |
Size: 9216 |
Author:yuandi |
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Description: maltab遗传算法源程序，此程序为一个一个的小程序分开的，很完整。包括编码，设定初始种群，交叉，变异，及结束条件等-maltab genetic algorithm source code, this procedure a small as a separate process, it is complete. Including encoding, set the initial population, crossover and mutation, and the end conditions Platform: |
Size: 7168 |
Author:joean |
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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: 为改进遗传算法的源程序，采用了多次2-opt变异算子，进一步改进了计算结果。-The source code for the improved genetic algorithm using a number of 2-opt mutation operator to further improve the results. Platform: |
Size: 2048 |
Author:碧天 |
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Description: 遗传算法车间调度,车间作业调度问题遗传算法
--------------------------------------------------------------------------
输入参数列表
M 遗传进化迭代次数
N 种群规模(取偶数)
Pm 变异概率
T m×n的矩阵，存储m个工件n个工序的加工时间
P 1×n的向量，n个工序中，每一个工序所具有的机床数目
输出参数列表
Zp 最优的Makespan值
Y1p 最优方案中，各工件各工序的开始时刻，可根据它绘出甘特图
Y2p 最优方案中，各工件各工序的结束时刻，可根据它绘出甘特图
Y3p 最优方案中，各工件各工序使用的机器编号
Xp 最优决策变量的值，决策变量是一个实数编码的m×n矩阵
LC1 收敛曲线1，各代最优个体适应值的记录
LC2 收敛曲线2，各代群体平均适应值的记录
最后，程序还将绘出三副图片：两条收敛曲线图和甘特图（各工件的调度时序图）-Genetic algorithm scheduling, job shop scheduling problems with genetic algorithms -------------------------------------------------------------------------- genetic evolution of the input parameter list, the number of iterations M N population size (taken even) Pm mutation probability T m × n matrix, stored m one piece n a process of processing time 1 × n vector, n a process in which each machine processes the number of Zp with the best value Y1p Optimal Makespan programs, the start time of each process the workpiece can be drawn based on its optimal solution Gantt Y2p, each time the workpiece end of the process, according to its draw Gantt Y3p optimal solution, each piece of the processes using machine code Xp optimal decision variable, decision variable is a real m × n matrix encoded LC1 convergence curve 1, the generation of the best individual record of LC2 fitness convergence curve 2, the average fitness value on behalf of groups record Finally, the program will draw three pict Platform: |
Size: 2048 |
Author:王龙隐 |
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Description: 自然计算中遗传算法的各个程序，matlab环境下开发的源代码。best.m 求种群中适应度最大的值
calfitvalue.m 计算每个个体的适应度
calobjvalue.m 适应度函数
crossover.m 交叉变换
decodebinary.m 将二进制数转换成十进制数
decodechrom.m 将二进制数转换成十进制数
initpop.m 产生初始种群
mutation.m 变异
selection.m 选择合适的个体进行复制
main.m 主函数
-Nature of each genetic algorithm calculation procedures, matlab environment with source code. best.m find the largest population in the fitness value of calfitvalue.m calculated for each individual' s fitness calobjvalue.m fitness function crossover.m cross-conversion decodebinary.m Converts a binary number into decimal number decodechrom.m Converts a binary number into decimal number initpop.m generate initial population mutation.m variation selection.m select the appropriate individual to copy main.m primary function Platform: |
Size: 3072 |
Author:王芳 |
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Description: 遗传算法求函数最值问题 matlab代码 使用轮盘赌选择 交叉 变异-Genetic algorithm for function most value problem matlab code crossover and mutation using the roulette wheel selection Platform: |
Size: 1024 |
Author:小新 |
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Description: 三个matlab实现的遗传算法，采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作-Three matlab genetic algorithm implemented using a binary Gray code, roulette method based on linear ranking selection, uniform crossover and mutation, but also introduces the inversion operation Platform: |
Size: 19456 |
Author:cici |
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Description: 基本蚁群聚类算法及其改进算法[带Matlab源代码]
该算法，解决了不收敛的问题，聚类效果非常好（效果图如附件图片所示）。
改进的蚁群算法是基于遗传算法的改进，在基本遗传算法的基础之上，加入了变异因子，
产生变异，从而更快的收敛。
-The basic ant clustering algorithm and its improved algorithm with Matlab source code of the algorithm to solve the non-convergence problems, the clustering effect is very good (image such as shown in the attached images). Improved ant colony algorithm is a genetic algorithm-based improvements in the basic genetic algorithm based on the added factor of variation, mutation, and thus faster convergence. Platform: |
Size: 260096 |
Author:陈魁 |
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Description: 整个遗传算法程序采用matlab编程，遗传算法的编码方式，适应度函数的选取，遗传操作（选择，交叉 变异），以及运行参数都有各自的备注和子程序代码。-The encoding of the entire genetic algorithm using matlab programming, genetic algorithm, the selection of the fitness function, genetic operations (selection, crossover and mutation), and operating parameters have their own notes and subroutine code. Platform: |
Size: 9216 |
Author:wang |
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Description: fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Fga.m main program for the genetic algorithm binary gray encoding, use based on roulette method of non-linear ranking selection, uniform crossover, mutation, and also introduced the inversion operation. Platform: |
Size: 11264 |
Author:Ma Chuan |
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Description: 本资源是Mgasa算法解决TSP问题的Matlab代码，资源中包括mgasa_main(Mgasa算法解决TSP问题代码)，mgasa_fitness(适应度求取函数代码)，mgasa_annealing(Mgasa算法中模拟退火代码)，mgasa_select(遗传算法中选择函数代码)，mgasa_crossover(遗传算法中染色体交叉互换函数代码)，mgasa_mutation(遗传算法中基因突变函数代码)，mgasa_change(Mgasa算法中选择过程代码)。同时代码中有Location矩阵，其中30个坐标作为TSP问题的例子。-This resource is Mgasa algorithm to solve the TSP problem matlab code, resources including the mgasa_main, the algorithm Mgasa solutions TSP code, mgasa_fitness (adaptation degree to obtain the function code), mgasa_annealing (Mgasa algorithm simulated annealing of code), mgasa_ (genetic algorithm selection function code), mgasa_crossover (genetic algorithm crossover swap function code), mgasa_mutation(genetic algorithm in gene mutation function code), mgasa_change(algorithm Mgasa selects the process of code). At the same time the code in the Location matrix, where 30 coordinates as an example of the TSP problem.
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Size: 4096 |
Author:lfr |
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Description: 用遗传算法实现函数最优化问题，程序源代码，包括适应度函数，选择，交叉，变异等函数文件。-Using genetic algorithm to realize the function optimization problem, the program source code, including the fitness function, selection, crossover, mutation function such as file Platform: |
Size: 6144 |
Author:yuanyuan |
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Description: 理解遗传算法的入门程序，这里面每一个程序的代码都有标注。便于理解生成种群、遗传和变异过程，附带了一个算例，可以参考。(Understand the entry procedure of genetic algorithm, in which every program's code is marked. It is easy to understand the process of population, heredity and mutation. A numerical example is provided for reference.) Platform: |
Size: 3072 |
Author:恰若烟雨 |
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Search - genetic algorithm mutation matlab code