Initial data commit

Task/Evolutionary-algorithm/R/evolutionary-algorithm-1.r

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+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)

Task/Evolutionary-algorithm/R/evolutionary-algorithm-2.r

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+# Setup
+set.seed(42)
+target= unlist(strsplit("METHINKS IT IS LIKE A WEASEL", ""))
+chars= c(LETTERS, " ")
+C= 100
+
+# Fitness function; high value means higher fitness
+fitness= function(x){
+  sum(x == target)
+}
+
+# Mutate function
+mutate= function(x, rate= 0.01){
+  idx= which(runif(length(target)) <= rate)
+  x[idx]= replicate(n= length(idx), expr= sample(x= chars, size= 1, replace= T))
+  x
+}
+
+# Evolve function
+evolve= function(x){
+  parents= rep(list(x), C+1) # Repliction
+  parents[1:C]= lapply(parents[1:C], function(x) mutate(x)) # Mutation
+  idx= which.max(lapply(parents, function(x) fitness(x))) # Selection
+  parents[[idx]]
+}
+
+# Initialize first parent
+parent= sample(x= chars, size= length(target), replace= T)
+
+# Main program
+while (fitness(parent) < fitness(target)) {
+  parent= evolve(parent)
+  cat(paste0(parent, collapse=""), "\n")
+}