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Genetic algorithms (or optimization) in R

I am trying to explore the field of optimization using GA in R. I understand that R is probably not the best place for me, but that is what I know right now.

So, here is the problem: I have a set of people who I can choose to build my home most efficiently. I am ready to spend no more than 100,000 on work (a limit beyond which the solution no longer applies), and I must choose one electrician, two plumbers, two general builders and two guys from the list of 500 or so (7 guys for work in total).

Each of these people has a rate that they will charge for work, and the total cost that they will provide for work (the total amount should be maximum). See data here:

Data

I looked at genalg and DeOptim R packages, where you can use binary functions to select people who maximize the total value for a given price, but I cannot determine how to set a limit on what type of employee is selected. The solution can be all electrician, and although they all provide great value, they cannot complete the job. So basically I'm looking for a way to manage the population, and none of these packages seem to allow this.

Any ideas on how I can solve this problem? I am sure that many of you have considered options for this problem and received excellent information. Thank you very much for your help.

Here is the code I got from the forums here:

library(genalg)

iter = 10
population = 500

# import csv file
brank = read.csv("GA_Data.csv")


dataset <- data.frame(item = brank$Person.ID, survivalpoints = brank$Value, weight = brank$Labor.Cost)


weightlimit <- 100000


monitor <- function(obj) {
  minEval = min(obj$evaluations);
  plot(obj$mean, obj$best, type="p", main = obj$iter); 
}

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)
}


GAmodel <- rbga.bin(size = length(brank$Person.ID), popSize = population, iters = iter, mutationChance = 0.01, 
                    monitorFunc = monitor, elitism = T, zeroToOneRatio = 200, evalFunc = evalFunc)

cat(summary.rbga(GAmodel))

########################
# STOP and replace "space" with "comma" in a text file

solution = c(0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0)
# dataset[solution == 1, ]
brank[solution == 1, ]

sum(brank$Value[solution == 1])
sum(brank$Labor.Cost[solution == 1])
+4
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1 answer

- (LP) . R , lpSolve.

D (299 ).

str(D)
# 'data.frame': 299 obs. of  4 variables:
#  $ ID   : int  1 2 3 4 5 6 7 8 9 10 ...
#  $ Type : Factor w/ 4 levels "Cleanup Guy",..: 4 4 4 4 4 4 4 4 4 4 ...
#  $ Cost : int  5100 3900 5000 4500 6700 5000 4000 3500 7500 4500 ...
#  $ Value: int  25 18 23 20 29 21 17 15 32 19 ...

# Prepare constraint matrix
A <- matrix(0, nrow = 5, ncol = 299)
A[1, c(1:27, 279:299)] <- 1     # Plumbers
A[2, 28:97] <- 1                # Electricians
A[3, 98:190] <- 1               # Const Workers
A[4, 191:278] <- 1              # Cleanup
A[5, ] <- D$Cost                # cost <= 100000

# Prepare input for LP solver
objective.in <- D$Value
const.mat <- A
const.dir <- c(">=", ">=", ">=", ">=", "<=")
const.rhs <- c(1, 2, 2, 2, 100000)

# Now solve the problem
require(lpSolve)
sol <- lp(direction = "max", objective.in, # maximize objective function
        const.mat, const.dir, const.rhs,   # constraints
        all.bin = TRUE)                    # use binary variables only

# View the solution
sol
## Success: the objective function is 589
sum(D$Cost[inds])
## 100000
inds <- which(sol$solution == 1)
D[inds, ]
##      ID         Type Cost Value
## 1     1      Plumber 5100    25
## 51   51  Electrician 5000    24
## 54   54  Electrician 3500    16
## 101 101 Const Worker 3500    18
## 102 102 Const Worker 4400    21
## 103 103 Const Worker 5800    27
## 147 147 Const Worker 5600    29
## 149 149 Const Worker 3700    17
## 196 196  Cleanup Guy 6200    30
## 197 197  Cleanup Guy 4200    20
## 256 256  Cleanup Guy 4000    27
## 263 263  Cleanup Guy 3800    22
## 264 264  Cleanup Guy 3800    33
## 266 266  Cleanup Guy 3700    23
## 267 267  Cleanup Guy 4200    30
## 273 273  Cleanup Guy 3600    35
## 274 274  Cleanup Guy 5900    28
## 275 275  Cleanup Guy 3700    28
## 282 282      Plumber 7000    37
## 287 287      Plumber 3800    30
## 297 297      Plumber 3600    37
## 298 298      Plumber 5900    32

. lp().

+5

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