add example of custom EA for METHINKS IT IS LIKE A WEASEL problem

inst/examples/weasel.R

@@ -0,0 +1,82 @@
+library(ecr)
+
+# Simple EA for R. Dawkin’s "METHINKS IT IS LIKE A WEASEL“ problem.
+# See http://rosettacode.org/wiki/Evolutionary_algorithm for details.
+
+# reproducibility
+set.seed(1)
+
+# optimal value. I.e., the target string.
+target = unlist(strsplit("METHINKS IT IS LIKE A WEASEL", ""))
+
+# character set the EA works on
+char.set = c(LETTERS, " ")
+
+# no smoof function since we use a custom representation here
+# ("string functions" not a default genotype)
+obj.fun = function(string, target) {
+  sum(string != target) / length(target)
+}
+
+# here we want to minimize the hamming distance
+task = makeOptimizationTask(obj.fun, n.objectives = 1L)
+
+# helper function to create a mutator for strings, i.e., character vectors.
+# It works by selecting genes, i.e., string positions, with a low probability p,
+# and replacing these with random values from the char.set.
+setupStringMutator = function(p) {
+  force(p)
+  makeMutator(
+    name = "String mutator",
+    mutator = function(ind, task, control) {
+      # get char.set
+      char.set = control$custom.constants$char.set
+      # determine which genes to mutate
+      idx = which(runif(length(ind)) < p)
+      n.mut = length(idx)
+      # perform mutation
+      if (n.mut > 0)
+        ind[idx] = sample(char.set, n.mut, replace = TRUE)
+      return(ind)
+    },
+    description = "",
+    supported = "custom" # non-standard representation
+  )
+}
+
+# generator function. Creates an initial population of random strings.
+stringGenerator = makeGenerator(
+  generator = function(size, task, control) {
+    # extract neccessary information
+    char.set = control$custom.constants$char.set
+    n = length(control$custom.constants$target)
+    makePopulation(lapply(seq(size), function(i) {
+      sample(char.set, length(target), replace = TRUE)
+    }))
+  },
+  name = "String generator",
+  description = "Generates random strings from a character set",
+  supported = "custom"
+)
+
+control = setupECRControl(
+  n.population = 1L, n.mating.pool = 1L, n.offspring = 100L,
+  representation = "custom",
+  # variables needed by some of the operators
+  custom.constants = list(char.set = char.set, target = target),
+  stopping.conditions = list(setupMaximumIterationsTerminator(100L))
+)
+
+control = setupEvolutionaryOperators(
+  control,
+  generator = stringGenerator,
+  mutator = setupStringMutator(p = 0.05),
+  recombinator = setupNullRecombinator(),
+  survival.selector = setupGreedySelector(),
+  parent.selector = setupGreedySelector()
+)
+
+res = doTheEvolution(task, control, more.args = list(target))
+
+print("Best parameter found:")
+print(paste(res$best.param, collapse = ""))