jezykr2

library(genalg)
#library(ggplot2)

dataset <- data.frame(item = c("pocketknife", "beans", "potatoes", "unions",
                               "sleeping bag", "rope", "compass"), survivalpoints = c(10, 20, 15, 2, 30,
                                                                                      10, 30), weight = c(1, 5, 10, 1, 7, 5, 1))
weightlimit <- 20

chromosome = c(1, 0, 0, 1, 1, 0, 0)
dataset[chromosome == 1, ]

cat(chromosome %*% dataset$survivalpoints)


evalFunc <- function(x) {
  current_solution_survivalpoints <- x %*% dataset$survivalpoints
  current_solution_weight <- x %*% dataset$weight

  if (current_solution_weight > weightlimit)
    return(0) else return(-current_solution_survivalpoints)
}


iter = 100
GAmodel <- rbga.bin(size = 7, popSize = 200, iters = iter, mutationChance = 0.01,
                    elitism = T, evalFunc = eval)
cat(summary.rbga(GAmodel))
solution = c(1, 1, 1, 1, 1, 0, 1)
dataset[solution == 1, ]
plot(-GAmodel$best,type = "l",col="red")
lines(-GAmodel$mean,col="blue")

c(1,0,0,0,1,0)
"10010"
a <- "0"
b <- "1"
b <- paste(a,b,sep="")

strtoi("001111000000", base = 2)
eval <- function(x){
x <- c(0,0,1,1)
xx <- ""
for(a in 1:lenght(x))
{
  xx <- paste(xx,x[a],sep= "")
}
xx.val <- strtoi(xx,base = 2)
return ((xx.val-2)^2)}

iter = 100
GAmodel <- rbga.bin(size = 7, popSize = 200, iters = iter, mutationChance = 0.01,
                    elitism = T, eval = eval)
cat(summary(GAmodel))
#solution = c(1, 1, 1, 1, 1, 0, 1)
#dataset[solution == 1, ]
#plot(-GAmodel$best,type = "l",col="red")
#lines(-GAmodel$mean,col="blue")


animate_plot <- function(x) {
  for (i in seq(1, iter)) {
    temp <- data.frame(Generation = c(seq(1, i), seq(1, i)), Variable = c(rep("mean",
                                                                              i), rep("best", i)), Survivalpoints = c(-GAmodel$mean[1:i], -GAmodel$best[1:i]))

    pl <- ggplot(temp, aes(x = Generation, y = Survivalpoints, group = Variable,
                           colour = Variable)) + geom_line() + scale_x_continuous(limits = c(0,
                                                                                             iter)) + scale_y_continuous(limits = c(0, 110)) + geom_hline(y = max(temp$Survivalpoints),
                                                                                                                                                          lty = 2) + annotate("text", x = 1, y = max(temp$Survivalpoints) +
                                                                                                                                                                                2, hjust = 0, size = 3, color = "black", label = paste("Best solution:",
                                                                                                                                                                                                                                       max(temp$Survivalpoints))) + scale_colour_brewer(palette = "Set1") +
      opts(title = "Evolution Knapsack optimization model")

    print(pl)
  }
}