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const NUM_CITIES = 10; // number of cities in the sales route
const POPULATION_SIZE = 100; // number of individuals in the population
const MUTATION_RATE = 0.1; // probability of mutation
const CROSSOVER_RATE = 0.7; // probability of crossover
const MAX_ITERATIONS = 1000; // maximum number of iterations

// define the distance between each pair of cities
const distances = [  [0, 10, 15, 20, 25, 30, 35, 40, 45, 50],
  [10, 0, 5, 10, 15, 20, 25, 30, 35, 40],
  [15, 5, 0, 5, 10, 15, 20, 25, 30, 35],

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[
  0, 1, 3, 4, 2,
  7, 6, 9, 8, 5
]

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// 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;

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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));
  // remove duplicate cities from the offspring
  let uniqueCities = new Set(offspring);
  offspring = [...uniqueCities];
  // if the offspring is too short, add cities from the other individual
  if (offspring.length < NUM_CITIES) {

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// define a function to select individuals for the next generation
const selection = (population) => {
  // calculate the fitness of each individual
  let fitnesses = population.map(calculateFitness);
  // normalize the fitness values
  let totalFitness = fitnesses.reduce((a, b) => a + b, 0);
  let probabilities = fitnesses.map((fitness) => fitness / totalFitness);
  // select individuals for the next generation
  let nextGeneration = [];

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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);

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// 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

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// create the initial population
let population = [];
for (let i = 0; i < POPULATION_SIZE; i++) {
  // generate a random individual
  let individual = [];
  for (let j = 0; j < NUM_CITIES; j++) {
    individual.push(j);
  }
  // shuffle the cities in the individual
  for (let j = 0; j < NUM_CITIES; j++) {

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const NUM_CITIES = 10; // number of cities in the sales route
const POPULATION_SIZE = 100; // number of individuals in the population
const MUTATION_RATE = 0.1; // probability of mutation
const CROSSOVER_RATE = 0.7; // probability of crossover
const MAX_ITERATIONS = 1000; // maximum number of iterations

// define the distance between each pair of cities
const distances = [  
  [0, 10, 15, 20, 25, 30, 35, 40, 45, 50], //distance of city 0 to all other cities
  [10, 0, 5, 10, 15, 20, 25, 30, 35, 40],//distance of city 1 to all other cities

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let internvalID = 0;
const names = ["Fernando", "John", "Cate", "Mary", "Francis"]

function sayHi(list) {
  let name = list[Math.round(Math.random() * 10) % 4]
  console.log("Hello", name)
}

intervalID = setInterval(sayHi, 1000, names)