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

64 lines
1.6 KiB
Dart

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();
}