warning: number of items to replace is not a multiple of replacement length

[swaheera]

I am working the R language. I am trying to follow this tutorial here (A quick tour of GA ) on function optimization.

I created some data for this example:

#load libraries
library(dplyr)
library(GA)

# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)

I defined the following function ("funct_set") over here:

funct_set <- function (x) {
    x1 <- x[1]; x2 <- x[2]; x3 <- x[3] ; x4 <- x[4]
    f <- numeric(4)
    
    
    #bin data according to random criteria
    train_data <- train_data %>%
        mutate(cat = ifelse(a1 <= x1 & b1 <= x3, "a",
                            ifelse(a1 <= x2 & b1 <= x4, "b", "c")))
    
    train_data$cat = as.factor(train_data$cat)
    
    #new splits
    a_table = train_data %>%
        filter(cat == "a") %>%
        select(a1, b1, c1, cat)
    
    b_table = train_data %>%
        filter(cat == "b") %>%
        select(a1, b1, c1, cat)
    
    c_table = train_data %>%
        filter(cat == "c") %>%
        select(a1, b1, c1, cat)
    
    
    
    #calculate  quantile ("quant") for each bin
    
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[5],1,0 )))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[6],1,0 )))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[7],1,0 )))
    
    f[1] = -mean(table_a$quant)
    f[2] = -mean(table_b$quant)
    f[3] = -mean(table_c$quant)
    
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
    # calculate the total mean : this is what needs to be optimized
    
    f[4] = -mean(final_table$quant)
    
    
    return (f);
}

Then, I ran the optimization algorithm:

GA <- ga(type = "real-valued", 
         fitness = funct_set,
         lower = c(80, 1, 80, 1, 90,180, 365), upper = c(100, 20, 100, 20, 140,400,720), 
         popSize = 50, maxiter = 20, run = 20)

This successfully runs, e.g. :

GA | iter = 1 | Mean = -0.9686663 | Best = -0.8636364
GA | iter = 2 | Mean = -0.9408230 | Best = -0.8571429
GA | iter = 3 | Mean = -0.9107766 | Best = -0.8571429
GA | iter = 4 | Mean = -0.8995899 | Best = -0.8571429

etc

But this produces the following warning:

There were 50 or more warnings (use warnings() to see the first 50)

When I view the warnings, I see this warning appear multiple times:

#view warnings
 warnings()

Warning messages:
1: In Fitness[i] <- fit :
  number of items to replace is not a multiple of replacement length
2: In Fitness[i] <- fit :
  number of items to replace is not a multiple of replacement length
3: In Fitness[i] <- fit :
  number of items to replace is not a multiple of replacement length

etc

Does anyone know why these warnings are being produced? Is there a way to avoid these warnings?

Thanks