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Description: 遗传算法进行优化求多元函数 （Griewank Function）最小解问题-genetic algorithm optimization for multi-function (Griewank Function) Minimum solutions to the problems
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Size: 1878 |
Author: 林言 |
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Description: 遗传算法进行优化求多元函数 （Griewank Function）最小解问题-genetic algorithm optimization for multi-function (Griewank Function) Minimum solutions to the problems
Platform: |
Size: 2048 |
Author: 林言 |
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Description: 遗传算法应用实例，求解函数的最值，及一些改进-Genetic algorithm applications, solve the function of the most value, and some improvement
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Size: 782336 |
Author: 何小攀 |
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Description: 基于遗传算法的函数极值求解，就是通过遗传算法求解函数极大极小值-use the GA algorithm to get the min or max vaual of the function
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Size: 257024 |
Author: 李斯定 |
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Description: 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(tour, 2)[, 2:1]
sum(distMatrix[route]) }
tpsFitness <- function(tour, ...) 1/tourLength(tour, ...)
GA.fit <- ga(type = permutation , fitness = tpsFitness, distMatrix = tspData, min = 1, max = 8, popSize = 10, maxiter = 500, run = 100, pmutation = 0.2, monitor = NULL)
summary(GA.fit)
-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(tour, 2)[, 2:1]
sum(distMatrix[route]) }
tpsFitness <- function(tour, ...) 1/tourLength(tour, ...)
GA.fit <- ga(type = permutation , fitness = tpsFitness, distMatrix = tspData, min = 1, max = 8, popSize = 10, maxiter = 500, run = 100, pmutation = 0.2, monitor = NULL)
summary(GA.fit)
Platform: |
Size: 2048 |
Author: peipei |
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