let index = 0;

  let r = random(1);

  while (r > 0) {

    r = r - population[index].fitness;

    index++;

  }

  index--;

  return population[index].brain;

}

​

​

​

let population = [];

let target;

​

let counter = 0;

​

function setup() {

  createCanvas(400, 400);

  ml5.tf.setBackend('cpu');

​

  for (let i = 0; i < 100; i++) {

    population[i] = new Particle();

  }

​

  target = createVector(350, 50);

}

​

​

function draw() {

  background(0);

​

  for (let p of population) {

    p.update();

    p.think();

    p.show();

  }

​

  counter++;

  if (counter == 100) {

    // next generation

    for (let p of population) {

      p.calculateFitness();

    }

    // Normalize Fitness

    let sum = 0;

    for (let p of population) {

      sum += p.fitness; 

    }

    for (let p of population) {

      p.fitness /= sum; 

    }

    let newPop = [];

    for (let i = 0; i < population.length; i++) {

      let brainA = pickOne();

      let brainB = pickOne();

      let childBrain = brainA.crossover(brainB);

      childBrain.mutate();

      newPop[i] = new Particle(childBrain);      

    }

    population = newPop;

    counter = 0;

  }

​

​

  fill(0, 255, 200);

  ellipse(target.x, target.y, 20);

​

​

}

​

function mousePressed() {

  target.x = mouseX;

  target.y = mouseY;  

}