Constrained Knapsnack problem with Genetic algorithm

Hello,

I have a very simple Knapsack problem. I would like to constrain it but I am not exactly sure how to get the GA package to do it. Essentially, I want to stop the optimization from being able to select more than one food item. So all best possible solutions can include one food item but not more than one. So in this case beans which is item 2 with profit 6 and weight 3 cannot be selected with "potatoes" which is item 3 with profit 6 and weight 3.

library(GA) 
library(tidyverse)

# Define data 
item = c("pocketknife", "beans", "potatoes", "unions", 
         "sleeping bag", "rope", "compass", "gps", "tar")
p <- c(6, 3, 6, 4, 6, 8, 6, 8, 10) # Profits 
w <- c(3, 3, 3, 7, 2, 3, 1, 4, 5) # Weights 
W <- 19 # Knapsack ’s capacity 
n <- length(p) # Number of items

# Define fitness function 
knapsack <- function(x) { 
  f <- sum(x * p) 
  penalty <- sum(w) * abs(sum(x * w) - W) 
}

#Genetic Algorithm--------------------------------------------------------------
genetic_alg_output <- ga(type="binary", 
                         fitness=knapsack , 
                         nBits=n, 
                         maxiter=200, # Maximum number of generations 
                         run=100,     # Stop if the best-so-far fitness
                         # hasn't improved for 'run' generations 
                         popSize=1000, 
                         keepBest = TRUE,
                         seed=101)

final_solutions <- genetic_alg_output@solution
print(final_solutions)