function evolve() {
  const population = generatePopulation();
  optimize(population);
}
            

// 神经网络架构搜索中的进化示例
const architecture = {
  layers: [
    { type: 'conv2d', filters: 32, kernel: 3 },
    { type: 'pool', size: 2 },
    { type: 'dense', units: 128 },
    { type: 'dropout', rate: 0.3 }
  ],
  fitness: 0.92  // 在验证集上的准确率
};
            

// 最简进化程序：猜字符串
const target = "HELLO WORLD";
const evo = new Evolver({
  geneLength: target.length,
  populationSize: 200,
  mutationRate: 0.02,
  fitnessFn: (genes) => {
    let score = 0;
    for (let i = 0; i < genes.length; i++) {
      if (genes[i] === target[i]) score++;
    }
    return score / target.length;
  }
});

const result = evo.run(1000);
console.log(result.best.genes.join(''));
// 输出: "HELLO WORLD" (通常 50-200 代即可)
            

class Evolver {
  constructor(config) {
    this.populationSize = config.populationSize;
    this.mutationRate = config.mutationRate;
    this.crossoverRate = config.crossoverRate;
    this.generations = 0;
  }

  run(maxGenerations) {
    let population = this.initialize();
    for (let i = 0; i < maxGenerations; i++) {
      const fitness = this.evaluate(population);
      population = this.select(population, fitness);
      population = this.crossover(population);
      population = this.mutate(population);
      this.generations++;
    }
    return this.getBest(population);
  }
}
            

evaluateFitness(individual) {
  let score = 0;
  const target = this.targetSolution;

  for (let i = 0; i < individual.genes.length; i++) {
    if (individual.genes[i] === target[i]) {
      score++;
    }
  }
  return score / target.length;
}
            

// 锦标赛选择实现
tournamentSelect(population, fitness, k=3) {
  let best = null;
  let bestFitness = -Infinity;

  for (let i = 0; i < k; i++) {
    const idx = Math.floor(
      Math.random() * population.length
    );
    if (fitness[idx] > bestFitness) {
      best = population[idx];
      bestFitness = fitness[idx];
    }
  }
  return best;
}
            

select(population, fitnessScores) {
  const selected = [];
  const totalFitness = fitnessScores.reduce(
    (sum, f) => sum + f, 0
  );

  for (let i = 0; i < population.length; i++) {
    let threshold = Math.random() * totalFitness;
    let cumulative = 0;
    for (let j = 0; j < population.length; j++) {
      cumulative += fitnessScores[j];
      if (cumulative >= threshold) {
        selected.push(population[j]);
        break;
      }
    }
  }
  return selected;
}
            

mutate(individual, mutationRate) {
  const mutated = { ...individual };
  const genes = [...mutated.genes];

  for (let i = 0; i < genes.length; i++) {
    if (Math.random() < mutationRate) {
      // 高斯变异：在当前值附近随机偏移
      genes[i] += gaussianRandom(0, 0.1);
      genes[i] = Math.max(0, Math.min(1, genes[i]));
    }
  }
  mutated.genes = genes;
  return mutated;
}
            

const config = {
  populationSize: 100,       // 种群规模
  mutationRate:  0.05,       // 变异概率
  crossoverRate: 0.8,        // 交叉概率
  maxGenerations: 500,       // 最大进化代数
  elitism: true,             // 是否保留最优个体
  selectionMethod: 'roulette'  // 选择策略
};
            

// 进化过程的统计追踪
class EvolutionTracker {
  constructor() {
    this.history = [];
    this.stagnationCount = 0;
    this.bestEver = { fitness: -Infinity };
  }

  record(generation, population, fitness) {
    const stats = {
      generation,
      bestFitness: Math.max(...fitness),
      avgFitness: fitness.reduce((s,f) => s+f, 0) / fitness.length,
      diversity: this.calcDiversity(population)
    };

    if (stats.bestFitness > this.bestEver.fitness) {
      this.bestEver = { ...stats };
      this.stagnationCount = 0;
    } else {
      this.stagnationCount++;
    }

    this.history.push(stats);
    return stats;
  }
}