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Here you can Sed posuere consectetur est at lobortis. Donec ullamcorper nulla non metus auctor fringilla. Maecenas sed diam eget risus varius blandit sit amet non magna. Donec id elit non mi porta gravida at eget metus. Praesent commodo cursus magna, vel scelerisque nisl consectetur et.

              // the desired result:
var target = "the quick brown fox jumped over the lazy dog";

// the set of components:
var dictionary = ["I", "a", "an", "bleep", "blown", "bog", "box", "broad", "brown", "bumped", "cat", "cog", "coloured", "colourful", "colourless", "crazy", "creep", "crown", "dig", "do", "dog", "door", "dot", "ever", "fit", "fix", "flick", "for", "fox", "frown", "glean", "great", "greed", "green", "greet", "hazy", "he", "hog", "ideas", "idols", "in", "it", "jumble", "jumped", "jumper", "jumps", "last", "lazy", "leaped", "lumped", "maze", "of", "offer", "ogre", "older", "on", "or", "over", "quick", "quite", "rapid", "she", "sheen", "sheep", "sick", "sleep", "slept", "slow", "spelt", "spilt", "the", "town", "under"];

// how many candidates in a generation:
var pop_size = 30;
// the base probability of mutations:
var gene_mutation_rate = 0.3;

// initialize:
var generation = 0;
// generate a random population:
var pop = [];
for (var i = 0; i < pop_size; i++) {
  var geno = [];
  for (var j = 0; j < 9; j++) {
    geno[j] = random(dictionary.length);

function develop(geno) {
  var pheno = [];
  for (var i = 0; i < geno.length; i++) {
    pheno[i] = dictionary[geno[i]];
  return pheno.join(" ");

function geno_eval(geno) {
  var pheno = develop(geno);

  geno.str = pheno;

  var err = 0;
  for (var i = 0; i < pheno.length; i++) {
    if (pheno.substr(i, 1) != target.substr(i, 1)) {
    // and backwards too?
    if (pheno.substr(pheno.length - 1 - i, 1) != target.substr(target.length - 1 - i, 1)) {

  geno.fitness = 1 / (1 + err / pheno.length);

function update() {
  // evaluate each member of the gene pool:
  for (var i = 0; i < pop_size; i++) {

  // sort by fitness:		
  pop.sort(function(a, b) {
    return b.fitness - a.fitness;

  // show them:
  for (var i = 0; i < pop_size; i++) {
    write(generation, i, pop[i].fitness, pop[i].str);

  // make a new generation:
  var newpop = [];
  for (var i = 0; i < pop_size; i++) {
    // fitter candidates are less volatile:
    var mutability = i / pop_size * gene_mutation_rate;

    var child = [];

    // pick a parent (by using random(i) we bias to fitter parents)
    var parent = pop[random(i)];

    // an alternative using proximity
    //var idx = i + random(9)-5;
    //idx = Math.min(Math.max(idx, 0), pop_size-1);
    //var parent = pop[ idx ];

    // copy or mutate genes:
    for (var j = 0; j < parent.length; j++) {
      // mutate?
      if (random() < mutability) {
        child[j] = random(dictionary.length);
      } else {
        // copy:
        child[j] = parent[j]
      // jumping gene:
      if (random() < mutability) {
          // remove a random gene (returns an array of length 1)
          var removed = child.splice(random(child.length), 1);
          // concatenate this at the end to create a new child:
          child = child.concat(removed);

    // add to population:
    newpop[i] = child;

  // replace:
  pop = newpop;
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