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AGA.rar
- 采用了保优的选择遗传算法 终止条件的判断是:到达一定的代数。可改进为:相邻若干代的种群平均适应值的变化来判断。若相邻若干代的种群平均适应值为变化或者是变化小于某一阈值,表示算法已经收敛,则退出算法。 选择算子:轮盘赌选择; 交叉算子:单点交叉,随机选择计算此适应度值,若大于当前最佳适应度值则降低交叉概率,否则不变; 变异算子:模板,对于优势个体,除采用低概率变异外,变异位置应采取权值越大,变异概率越小的原则,而对劣势个体则相反.,Paul used the choice of excel
yichuansuanfa_01
- 用C#实现了最基本的遗传算法,即单节点交叉变异的遗传算法。可以通过阅读代码自行修改-Using C# to achieve the most basic genetic algorithm, that is a single node in the genetic algorithm crossover and mutation. You can modify the code by reading
mktest.rar
- m-k检验,用于进行m-k检验 突变所发生的时间,mk test, used for testing mk mutation occurred at a time
TSP.rar
- 采用visual c解决tsp问题。里面有遗传算法的选择、交叉、变异函数。,Using visual c solve the problem tsp. There are genetic algorithm selection, crossover and mutation functions.
improvedPSO.rar
- ------名称:带交叉因子的改进PSO算法 ------功能:求解多维无约束优化问题 ------特点:收敛性强,还可以加入变异算子,------ Name: with cross-factor function to improve the PSO algorithm ------: Solving multi-dimensional unconstrained optimization problem ------ characteristics: strong converge
BP_MATLAB
- 包括种群初始化、选择、交叉、变异各个子程序,每一部分都作为一个单独M文件。主程序是实现遗传算法网络的训练过程;测试程序可以对训练得到的网络进行结果测试-Including population initialization, selection, crossover and mutation all subroutines, each of which as a separate M-file. Main program is to achieve genetic algorithm netwo
myGA
- matlab编的改进遗传算法的程序,对交叉算子和变异算子进行了自适应改进-Improved genetic algorithm matlab code procedure, the crossover operator and mutation operator to improve the adaptive
sga
- 基本遗传算法的matlab源程序,bstr2rval.m为基本编码转为实际值子函数,createPop.m创建初始种群子函数,crossOper.m基因交叉子函数,indiEval.m个体实际值子函数,mutateOper.m基因变异子函数,selectOper.m选择算子子函数,sga.m基本遗传算法子函数-The basic genetic algorithm matlab source, bstr2rval.m as the basic coding to the actual value
QGA
- 这是一个非常简单的遗传算法源代码,是由Denis Cormier (North Carolina State University)开发的,Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少,实际上也不必查错。对一特定的应用修正此代码,用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值,其中的目标函数只能取正值;且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交
Solving0-Hybrid-Scheduling-Problems-Job_Shop-adapt
- 一种求解Job_Shop调度问题的混合自适应变异粒子群算法-Solving Hybrid Scheduling Problems Job_Shop adaptive mutation particle swarm optimization
chap1
- 基于遗传算法的PID参数优化的MATLAB程序,采用的是二进制编码方式,通过传递函数的离散经过适应度计算 选择复制 交叉 变异操作得到最优解。-PID parameters based on genetic algorithm optimization of the MATLAB program, using the binary encoding, by the discrete transfer function adapted calculation select Copy crossov
MATLAB-genetic-algorithm-toolbox
- 介绍遗传算法的原理,流程。详细展示了交叉,变异,选择等算子。同时,还介绍了谢菲尔德大学遗传工具箱的使用 。本书对初学遗传算法者很有帮助。-Introduce the principle of genetic algorithms, processes. Detail shows crossover, mutation, selection operator. Meanwhile, the University of Sheffield also introduced the use of gen
GA
- 遗传算法MATLAB源程序,其中用到了交叉选择变异等操作-MATLAB source of genetic algorithm, which uses cross-selection and mutation operations
RealGeneticAlgorithm
- 自已编写的c#自适应实数编码遗传算法,提供多种交叉变异和选择方法,应用时结合需要,修改适应度函数即可!-Write their own adaptive real-coded genetic algorithms, offers a variety of crossover and mutation and selection methods, application integration needs, modify the fitness function can be!
GA_only-mutation
- 遗传算法解决车辆路径问题,用matlab解答,可以直接运行-Genetic algorithm can solve the vehicle routing problem
xiaoboji
- 采用低通性质的平滑函数为高斯函数,根据他的一阶导数、二阶导数作为小波基函数进行突变点分析。 -The use of low-pass nature of the smoothing function for the Gaussian function, according to his first derivative, second derivative wavelet basis function as a point mutation analysis.
genetic
- 本文件为遗传算法的工具箱,包括遗传算法的各种选择、交叉、变异等算法。-This document is for the genetic algorithm toolbox, including a variety of genetic algorithm selection, crossover, mutation and other algorithms.
climatefortranprogramm
- 该程序用fortran语言编写,可进行滑动平均,突变检验,周期诊断等-The procedure used fortran language, may be moving average, mutation testing, diagnosis, such as cycle
mhsj
- 针对遗传算法的特点,提出一种用模糊控制的方法来调整交叉概率和变异概率的改进模糊遗传算法及其算法结构,并 将其应用于神经模糊控制器的综合优化设计。-For the characteristics of genetic algorithm, a method using fuzzy control to adjust the crossover probability and mutation probability of improving the fuzzy genetic algorithm
test
- 包括建初始种群,编码,适应值计算,以及交叉、变异、重组等源码-Including building the initial population, coding, adaptation values, as well as crossover and mutation or re-source