add knapsack example

inst/examples/knapsack_example.R

@@ -0,0 +1,61 @@
+library(ecr)
+library(plyr)
+library(reshape2)
+
+set.seed(1)
+
+# knapsack with 10 objects A, B, ..., J with weight w_i and value v_i
+ks = data.frame(
+  object = LETTERS[1:10],
+  weight = c(10, 11, 4, 2, 2, 9, 3, 5, 15, 4),
+  value  = c(5, 10, 1, 6, 3, 5, 3, 19, 23, 1)
+)
+
+# knapsack capacity
+ks.limit = 25
+
+# objective function to be maximized, i.e., total value of bagged objects,
+# under the restriction of the total weight being lower than the capacity.
+obj.fun = makeSingleObjectiveFunction(
+  fn = function(x) {
+    val = sum(x * ks$value)
+    weight = sum(x * ks$weight)
+    if (weight > ks.limit)
+      return(0)
+    return(val)
+  },
+  minimize = FALSE, # here we maximize
+  par.set = makeParamSet(
+    makeIntegerVectorParam(id = "x", lower = 0, upper = 1, len = nrow(ks))
+  )
+)
+
+# use "natural" binary representation x = (x_1, ..., x_n) with x_i = 1 means,
+# that object i is bagged and x_i = 0 otherwise.
+control = setupECRControl(
+  representation = "binary",
+  n.population = 25,
+  n.offspring = 10,
+  monitor = NULL,
+  logger = setupOptPathLoggingMonitor(),
+  survival.strategy = "plus",
+  stopping.conditions = list(setupMaximumIterationsTerminator(max.iter = 100L))
+)
+
+# run algorithm
+res = doTheEvolution(obj.fun, control)
+
+# extract EA knapsack solution
+print(res$best.value)
+print(res$best.param)
+print(ks[as.logical(res$best.param), ])
+
+# plot optimization path
+op = as.data.frame(res$opt.path)
+op = ddply(op, .variables = c("dob"), summarize, mean = mean(y), max = max(y))
+op = melt(op, "dob", value.name = "Value", variable.name = "Stat")
+
+pl = ggplot(op, aes(x = dob, y = Value, colour = Stat)) + geom_line()
+pl = pl + xlab("Generation") + ylab("Total value")
+print(pl)
+#ggsave(filename = "KP_trace.pdf", width = 6, height = 3)