In this example, I am trying to optimize the custom function "my_subset_mean" by using the "nb" (custom) function and the "TAopt" algorithm:

library(dplyr)

#create data

df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),

c = rnorm(100,10,10))`a <- c("a", "b", "c", "d", "e") a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1)) df$a <- a e <- c("a", "b", "c", "d", "e") e <- sample(e, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1)) df$a <- e #create function to be optimized my_subset_mean <- function(x){ subset <- df %>% filter(a %in% names(x$r1)[x$r1], e %in% names(x$r4)[x$r4], b > x$r2, d < x$r3) ans <- -mean(subset$c) if (!is.finite(ans)) ans <- 100 ans } #store values of categorical variables into temporary objects tmp <- !logical(length(sort(unique(a)))) names(tmp) <- sort(unique(a)) tmp1 <- !logical(length(sort(unique(e)))) names(tmp1) <- sort(unique(e)) x <- list(r1 = tmp, r4 = tmp1, r2 = 0.5, r3 = 0.5) ### optimization nb <- function(x) { i <- sample(c("r1", "r2", "r3", "r4"), 1) if (i == "r1" & i == "r4") { j <- sample(length(x[[i]]), 1) x[[i]][j] <- !x[[i]][j] } else { x[[i]] <- x[[i]] + runif(1, min = -0.1, max = 0.1) x[[i]] <- max(min(1, x[[i]]), 0) } x } library("NMOF") ans <- TAopt(my_subset_mean, list(x0 = x, neighbour = nb, nI = 1000)) -my_subset_mean(ans$xbest)`

Can someone please tell me - in this process, where would you do specify the upper and lower bounds for r2 and r3? For example, if I want to specify that r2 between (0,2) and r3 between (0,1.5) - where exactly can I specify this?

Thanks!