遗传算法在R
问题描述:
我有这样的问题: 我需要找到不超过最大重量的项目的最佳组合。 对于这个问题我使用遗传算法。遗传算法在R
这里是我的数据
dataset <- data.frame(name = paste0("x",1:11),
Weight = c(2.14083022,7.32592911,0.50945094,4.94405846,12.02631340,14.59102403,0.07583312,0.36318323,10.64413370,3.54882187,1.79507759),
stringsAsFactors = F)
这里是我的成本函数:
max_weight = 10
fitness_function <- function(x){
current_weight <- x %*% dataset$Weight
if (current_weight > max_weight){
return(0)
} else {
return(-1* current_weight)
}
}
然后我试图从两个包GA:genalg
和GA
genalg
ga_genalg <- rbga.bin(size = 11,
popSize = 100,
mutationChance = .1,
evalFunc = fitness_function)
好,这里是结果:
cat(summary(ga_genalg))
GA Settings
Type = binary chromosome
Population size = 100
Number of Generations = 100
Elitism = 20
Mutation Chance = 0.1
Search Domain
Var 1 = [,]
Var 0 = [,]
GA Results
Best Solution : 0 1 1 0 0 0 0 1 0 0 1
我查了最佳的解决方案,看起来不错:
genalg_best_solution = c(0,1,1,0,0,0,0,1,0,0,1)
dataset$Weight %*% genalg_best_solution
[,1]
[1,] 9.993641
PS。任何人都知道如何得到这个最佳的解决方案载体,而无需输入正则表达式?
GA
ga_GA <- ga(type = "binary", fitness = fitness_function, popSize = 100, pmutation = .1, nBits = 11)
ga_best_solution = [email protected]
dim(ga_best_solution)
[1] 73 11
解是矩阵73点的行。也[email protected]
返回list()
我在这个包最好的解决方案在哪里?或者我需要检查所有73行,找到最好的(我试过,得到73个零)?
PPS。第二个问题解决方案:GA最大化函数和genalg最小化函数= /。 任何人都知道如何从genalg包中提取最佳解决方案?
答
这里有很多问题。我的意见是,GA为您提供了更容易的输出:最佳解决方案和健身评分。
你说得对,遗传算法最大化健身得分,而genalg最小化 - 我创建了第二个适应度函数,它不返回适应值乘以-1。这导致两者都有相同的解决方案。
此外,我没有得到您为ga()输出呈现的维度。在我的情况下,这只是一个单行与11个二进制值:
library(GA)
library(genalg)
dataset <- data.frame(name = paste0("x",1:11),
Weight = c(
2.14083022,7.32592911,0.50945094,4.94405846,
12.02631340,14.59102403,0.07583312,0.36318323,
10.64413370,3.54882187,1.79507759
),
stringsAsFactors = F
)
max_weight = 10
# genalg ------------------------------------------------------------------
# fitness function for genalg
fitness_function <- function(x){
current_weight <- x %*% dataset$Weight
if (current_weight > max_weight){
return(0)
} else {
return(-current_weight)
}
}
ga_genalg <- rbga.bin(size = 11,
popSize = 100,
mutationChance = .1,
evalFunc = fitness_function
)
tail(ga_genalg$best, 1) # best fitness
summary(ga_genalg, echo=TRUE)
plot(ga_genalg) # plot
# helper function from ?rbga.bin
monitor <- function(obj) {
minEval = min(obj$evaluations);
filter = obj$evaluations == minEval;
bestObjectCount = sum(rep(1, obj$popSize)[filter]);
# ok, deal with the situation that more than one object is best
if (bestObjectCount > 1) {
bestSolution = obj$population[filter,][1,];
} else {
bestSolution = obj$population[filter,];
}
outputBest = paste(obj$iter, " #selected=", sum(bestSolution),
" Best (Error=", minEval, "): ", sep="");
for (var in 1:length(bestSolution)) {
outputBest = paste(outputBest,
bestSolution[var], " ",
sep="");
}
outputBest = paste(outputBest, "\n", sep="");
cat(outputBest);
}
monitor(ga_genalg)
# 100 #selected=4 Best (Error=-9.99364087): 0 1 1 0 0 0 0 1 0 0 1
# GA ----------------------------------------------------------------------
# fitness function for GA (maximizes fitness)
fitness_function2 <- function(x){
current_weight <- x %*% dataset$Weight
if (current_weight > max_weight){
return(0)
} else {
return(current_weight)
}
}
ga_GA <- ga(type = "binary", fitness = fitness_function2, popSize = 100, pmutation = .1, nBits = 11)
[email protected]
# x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11
# [1,] 0 1 1 0 0 0 0 1 0 0 1
dim(ga_best_solution)
# [1] 1 11
[email protected]
# [1] 9.993641
嗨马克,伟大的答案在那里!你能解释'fitness_function2(x)'采用的参数'x'吗? x是染色体吗?在'ga()'函数中,为什么不需要传递'x'作为参数,即'fitness = fitness_function2'? –
@jacky_learns_to_code - 是的,这是由nBits参数定义的基因向量(在本例中为二进制)。 –
有没有办法控制基因的行为,这样我的nBits' = 5000,但是'1'的数量总是= 10? –