Genetic-Algorithms/target-ga.html at main · thinkphp/Genetic-Algorithms · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>Target String GA</title>
  <style>
    body { font-family: sans-serif; padding: 20px; }
    .gene { font-size: 24px; font-family: monospace; }
  </style>
</head>
<body>

<h1>Target String Genetic Algorithm</h1>
<p>Target: <strong id="target"></strong></p>
<p>Generation: <span id="generation">0</span></p>
<p>Best Match: <span class="gene" id="best"></span></p>
<p>Fitness: <span id="fitness">0</span></p>

<script>
  const target = "Hello, world!";
  const populationSize = 200;
  const mutationRate = 0.01;
  const charset = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz,.!? ";
  const geneLength = target.length;

  document.getElementById("target").textContent = target;

  function randomChar() {
    return charset.charAt(Math.floor(Math.random() * charset.length));
  }

  function randomGene() {
    return Array.from({ length: geneLength }, randomChar).join('');
  }

  function fitness(gene) {
    let score = 0;
    for (let i = 0; i < gene.length; i++) {
      if (gene[i] === target[i]) score++;
    }
    return score / geneLength;
  }

  function crossover(parentA, parentB) {
    const midpoint = Math.floor(Math.random() * geneLength);
    let child = "";
    for (let i = 0; i < geneLength; i++) {
      child += (i < midpoint ? parentA[i] : parentB[i]);
    }
    return mutate(child);
  }

  function mutate(gene) {
    return gene.split('').map(ch => Math.random() < mutationRate ? randomChar() : ch).join('');
  }

  let generation = 0;
  let population = Array.from({ length: populationSize }, randomGene);

  function evolve() {
    generation++;
    const sorted = population
      .map(g => ({ gene: g, fit: fitness(g) }))
      .sort((a, b) => b.fit - a.fit);

    const best = sorted[0];
    document.getElementById("generation").textContent = generation;
    document.getElementById("best").textContent = best.gene;
    document.getElementById("fitness").textContent = (best.fit * 100).toFixed(2) + "%";

    if (best.gene === target) {
      clearInterval(interval);
      alert("Target reached in " + generation + " generations!");
      return;
    }

    // Select top 20% as parents
    const parents = sorted.slice(0, populationSize * 0.2).map(p => p.gene);

    // Reproduce
    population = Array.from({ length: populationSize }, () => {
      const a = parents[Math.floor(Math.random() * parents.length)];
      const b = parents[Math.floor(Math.random() * parents.length)];
      return crossover(a, b);
    });
  }

  const interval = setInterval(evolve, 50); // evolve every 50 ms
</script>

</body>
</html>