// Daniel Shiffman

// http://natureofcode.com

​

// Genetic Algorithm, Evolving Shakespeare

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// Demonstration of using a genetic algorithm to perform a search

​

// setup()

//  # Step 1: The Population

//    # Create an empty population (an array or ArrayList)

//    # Fill it with DNA encoded objects (pick random values to start)

​

// draw()

//  # Step 1: Selection

//    # Create an empty mating pool (an empty ArrayList)

//    # For every member of the population, evaluate its fitness based on some criteria / function,

//      and add it to the mating pool in a manner consistant with its fitness, i.e. the more fit it

//      is the more times it appears in the mating pool, in order to be more likely picked for reproduction.

​

//  # Step 2: Reproduction Create a new empty population

//    # Fill the new population by executing the following steps:

//       1. Pick two "parent" objects from the mating pool.

//       2. Crossover -- create a "child" object by mating these two parents.

//       3. Mutation -- mutate the child's DNA based on a given probability.

//       4. Add the child object to the new population.

//    # Replace the old population with the new population

//

//   # Rinse and repeat

​

​

let target;

let popmax;

let mutationRate;

let populations = [];

​

let bestPhrase;

let allPhrases;

let stats;

let answer;

let x = true;

​

function setup() {

  createCanvas(200,200);

  background(255,0,255,200);

  bestPhrase = createP("Best phrase:");

  //bestPhrase.position(10,10);

  bestPhrase.class("best");

​

  allPhrases = createP("All phrases:");

  allPhrases.position(600, 10);

  allPhrases.class("all");

​

  stats = createP("Stats");

  //stats.position(10,200);

  stats.class("stats");

​

  //createCanvas(640, 360);

  target = "255,000,255";

  popmax = 50;

  mutationRate = 0.01;

  // console.log(target);

  // Create a population with a target phrase, mutation rate, and population max

  for(let i=0; i<2; i++){

  populations[i] = new Population(target, mutationRate, popmax);

  }

  // frameRate(2);

}

​

function draw() {

  // background(255,0,255,200);

  for(let i=0; i<2; i++){

  // Generate mating pool

  populations[i].naturalSelection();

  //Create next generation

  populations[i].generate();

  // Calculate fitness

  populations[i].calcFitness();

​

  populations[i].evaluate();

​

  // If we found the target phrase, stop

  if (populations[i].isFinished()) {

    //println(millis()/1000.0);

    // noLoop();

  }

​

  displayInfo(populations[i], i);

  }

}

​

function displayInfo(pop, i) {

  // Display current status of population

  answer = pop.getBest();

  // bestPhrase.html("Best color:<br>" + answer);

​

  // let statstext = "total generations:     " + population.getGenerations() + "<br>";

  // statstext += "average fitness:       " + nf(population.getAverageFitness()) + "<br>";

  // statstext += "total population:      " + popmax + "<br>";

  // statstext += "mutation rate:         " + floor(mutationRate * 100) + "%";

​

  // stats.html(statstext);

​

  // allPhrases.html("All colors:<br>" + population.allPhrases())

  c11 = answer.charAt(0);

  c12 = answer.charAt(1);

  c13 = answer.charAt(2);

  c21 = answer.charAt(4);

  c22 = answer.charAt(5);

  c23 = answer.charAt(6);

  c31 = answer.charAt(8);

  c32 = answer.charAt(9);

  c33 = answer.charAt(10);

  // console.log(x,y,z);

  let r = int(c11+c12+c13);

  let g = int(c21+c22+c23);

  let b = int(c31+c32+c33);

  // console.log(r,g,b);

  fill(r,g,b);

  noStroke();

  ellipse((i+1)*width/3, (i+1)*height/3, 20,20);

}

​

function mousePressed(){

  x=!x;

  if(!x){

  background(147,180,130);

  target = "147,180,130";

  } else {

  background(255,000,255);

  target = "255,000,255";

  }

  // displayInfo();

}