搜索资源列表
ga
- GA算法代码 function pop=initpop(popsize,chromlength) pop=round(rand(popsize,chromlength)) rand 随机产生每个单元为 {0,1} 行数为 popsize，列数为 chromlength 的矩阵， roud 对矩阵的每个单元进行圆整。这样产生的初始种群。 2.2 计算目标函数值 2.2.1 将二进制数转化为十进制数(1) 遗传算法子程序 Name: decodebinary.
fitnessfun
- a novel fitness function for utilization for images segmentation using a metaheuristic method (GA, pso, sfla, aco...)
A-hybrid
- 针对传统的ＢＰ或ＧＡ对模糊神经网络的识别应用存在收敛容易陷入局部极小 识别率低下等问题 提出一 种基于ＢＦＧＳ的混合遗传算法 其基本思想为 首先构造一种前馈型模糊神经网络结构 然后用遗传算法进化若干代 后 当目标函数的梯度或者范数小于预先设定值 则改用ＢＦＧＳ算法进行优化识别 仿真实验表明 对比ＧＡ该算法 收敛速度较快 识别精度提高了约７％ 能够较好地应用于一类模糊神经网络的识别-In traditional BP or GA to identify the application
A-Novel-Multi-focus---Image--Fusion
- We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, relative to the clarity function, the optimum block from amo
GA-Elitst
- 基于精英策略的简单遗传算法代码.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
yichuansuanfa
- 遗传算法（Genetic Algorithm，GA）是通过对自然界中生物的遗传和优胜劣汰的进化过程进行模拟与抽象，进而形成的一种自适应全局随机优化搜索方法。遗传算法只需提供目标函数作为寻优信息，它从某一随机生成的初始群体出发，经过选择、交叉和变异等遗传操作后对个体进行适应度评价，保留适应度较强的个体遗传到子代种群中，经过多次的迭代计算求得最优个体，即问题的最优解。本程序采用遗传算法可求解微网优化运行。-Genetic Algorithm is an adaptive global by natu
shishubianma
- 遗传算法是兼职编码求函数的极大值程序，比较详细。-A function or part-time coding genetic algorithm (ga) is a great value program, more detail.
ga
- The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individ
weka
- tspData <- read.csv( D:\\weka\\hw\\TSP.csv , header = T, sep = , ) #tspData <- `colnames<-`(tspData,c(1:8)) D <- as.matrix(tspData) tourLength <- function(tour, distMatrix) { tour <- c(tour, tour[1]) route <- embed(tou
06341870
- Abstract—This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load d
GA-PSO
- 粒子群算法与遗传算法的联合的GA-PSO算法运用，带有测试函数-Joint GA-PSO algorithm using particle swarm optimization and genetic algorithm with test function