second-part2.js

// define a function to perform crossover between two individuals
const crossover = (individual1, individual2) => {
  // choose a random crossover point
  let point = Math.floor(Math.random() * NUM_CITIES);
  // create the offspring by combining the two individuals
  let offspring = individual1.slice(0, point).concat(individual2.slice(point));
  // return the offspring
  return offspring;
};

// define a function to perform mutation on an individual
const mutation = (individual) => {
  // iterate over the cities in the individual
  for (let i = 0; i < NUM_CITIES; i++) {
    // with probability MUTATION_RATE, swap this city with another city
    if (Math.random() < MUTATION_RATE) {
      let j = Math.floor(Math.random() * NUM_CITIES);
      let temp = individual[i];
      individual[i] = individual[j];
      individual[j] = temp;
    }
  }
  // return the mutated individual
  return individual;
};

// define a function to evolve the population
const evolve = (population) => {
  // select individuals for the next generation
  let nextGeneration = selection(population);
  // create the new generation by performing crossover and mutation
  for (let i = 0; i < POPULATION_SIZE; i++) {
    // with probability CROSSOVER_RATE, perform crossover
    if (Math.random() < CROSSOVER_RATE) {
      let individual1 = nextGeneration[i];
      let individual2 = nextGeneration[Math.floor(Math.random() * POPULATION_SIZE)];
      nextGeneration[i] = crossover(individual1, individual2);
    }
    // perform mutation on the individual
    nextGeneration[i] = mutation(nextGeneration[i]);
  }
  // return the new generation
  return nextGeneration;
};

// run the genetic algorithm for a specified number of iterations
for (let i = 0; i < MAX_ITERATIONS; i++) {
  population = evolve(population);
}

// sort the population by fitness
population.sort((a, b) => calculateFitness(a) - calculateFitness(b));

// print the best individual
console.log(population[0]);