June 2018 Update

Task/Evolutionary-algorithm/R/evolutionary-algorithm.r

@@ -1,52 +0,0 @@
-set.seed(1234, kind="Mersenne-Twister")
-
-## Easier if the string is a character vector
-target <- unlist(strsplit("METHINKS IT IS LIKE A WEASEL", ""))
-
-charset <- c(LETTERS, " ")
-parent <- sample(charset, length(target), replace=TRUE)
-
-mutaterate <- 0.01
-
-## Number of offspring in each generation
-C <- 100
-
-## Hamming distance between strings normalized by string length is used
-## as the fitness function.
-fitness <- function(parent, target) {
-    sum(parent == target) / length(target)
-}
-
-mutate <- function(parent, rate, charset) {
-    p <- runif(length(parent))
-    nMutants <- sum(p < rate)
-    if (nMutants) {
-        parent[ p < rate ] <- sample(charset, nMutants, replace=TRUE)
-    }
-    parent
-}
-
-evolve <- function(parent, mutate, fitness, C, mutaterate, charset) {
-    children <- replicate(C, mutate(parent, mutaterate, charset),
-                          simplify=FALSE)
-    children <- c(list(parent), children)
-    children[[which.max(sapply(children, fitness, target=target))]]
-}
-
-.printGen <- function(parent, target, gen) {
-    cat(format(i, width=3),
-        formatC(fitness(parent, target), digits=2, format="f"),
-        paste(parent, collapse=""), "\n")
-}
-
-i <- 0
-.printGen(parent, target, i)
-while ( ! all(parent == target)) {
-    i <- i + 1
-    parent <- evolve(parent, mutate, fitness, C, mutaterate, charset)
-
-    if (i %% 20 == 0) {
-        .printGen(parent, target, i)
-    }
-}
-.printGen(parent, target, i)