// Daniel Shiffman

// http://natureofcode.com

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// Genetic Algorithm, Evolving Shakespeare

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

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// setup()

//  # Step 1: The Population

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

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

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// 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.

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//  # 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

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// Mutation rate

let mutationRate = 0.01;

// Population Size

let populationSize = 150;

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// Population array

let population = [];

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// Target phrase

let target = "to be or not to be";

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function setup() {

  createCanvas(640, 240);

  //{!3} Step 1: Initialize Population

  for (let i = 0; i < populationSize; i++) {

    population[i] = new DNA(target.length);

  }

}

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function draw() {

  // Step 2: Selection

  //{!3} Step 2a: Calculate fitness.

  for (let phrase of population) {

    phrase.calculateFitness(target);

  }

​

  // Step 2b: Build mating pool.

  let matingPool = [];

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  for (let phrase of population) {

    //{!4} Add each member n times according to its fitness score.

    let n = floor(phrase.fitness * 100);

    for (let j = 0; j < n; j++) {

      matingPool.push(phrase);

    }

  }

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  // Step 3: Reproduction

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

    let partnerA = random(matingPool);

    let partnerB = random(matingPool);

    // Step 3a: Crossover

    let child = partnerA.crossover(partnerB);

    // Step 3b: Mutation

    child.mutate(mutationRate);

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    //{!1} Note that we are overwriting the population with the new

    // children.  When draw() loops, we will perform all the same

    // steps with the new population of children.

    population[i] = child;

  }

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  let everything = "";

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

    everything += population[i].getPhrase() + "    ";

  }

  background(255);

  textFont("Courier");

  textSize(12);

  text(everything, 12, 0, width, height);

}

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