Data update

Task/Evolutionary-algorithm/Dart/evolutionary-algorithm.dart

@@ -0,0 +1,63 @@
+import 'dart:math';
+
+class EvoAlgo {
+  static final String target = "METHINKS IT IS LIKE A WEASEL";
+  static final List<String> possibilities = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".split('');
+  static int c = 100; // Number of spawn per generation
+  static double minMutateRate = 0.09;
+  static int perfectFitness = target.length;
+  static String parent = '';
+  static Random rand = Random();
+
+  static int fitness(String trial) {
+    int retVal = 0;
+    for (int i = 0; i < trial.length; i++) {
+      if (trial[i] == target[i]) retVal++;
+    }
+    return retVal;
+  }
+
+  static double newMutateRate() {
+    return (((perfectFitness - fitness(parent)) / perfectFitness) * (1 - minMutateRate));
+  }
+
+  static String mutate(String parent, double rate) {
+    String retVal = '';
+    for (int i = 0; i < parent.length; i++) {
+      retVal += (rand.nextDouble() <= rate)
+          ? possibilities[rand.nextInt(possibilities.length)]
+          : parent[i];
+    }
+    return retVal;
+  }
+
+  static void main() {
+    parent = mutate(target, 1);
+    int iter = 0;
+    while (parent != target) {
+      double rate = newMutateRate();
+      iter++;
+      if (iter % 100 == 0) {
+        print('$iter: $parent, fitness: ${fitness(parent)}, rate: $rate');
+      }
+      String bestSpawn;
+      int bestFit = 0;
+      for (int i = 0; i < c; i++) {
+        String spawn = mutate(parent, rate);
+        int fit = fitness(spawn);
+        if (fit > bestFit) {
+          bestSpawn = spawn;
+          bestFit = fit;
+        }
+      }
+      if (bestFit > fitness(parent)) {
+        parent = bestSpawn;
+      }
+    }
+    print('$parent, $iter');
+  }
+}
+
+void main() {
+  EvoAlgo.main();
+}