// The Nature of Code

// The Coding Train / Daniel Shiffman

// https://youtu.be/HzaLIO9dLbA

// https://thecodingtrain.com/more/achive/nature-of-code/9-genetic-algorithms/9.6-improved-fitness-function.html

// https://editor.p5js.org/codingtrain/sketches/xkrHRPSKM

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// 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)

​

// 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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let target;

let popmax;

let mutationRate;

let population;

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let bestPhrase;

let allPhrases;

let stats;

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function setup() {

  bestPhrase = createP("Best phrase:");

  //bestPhrase.position(10,10);

  bestPhrase.class("best");

​

  allPhrases = createP("All phrases:");

  allPhrases.position(600, 10);

  allPhrases.class("all");

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  stats = createP("Stats");

  //stats.position(10,200);

  stats.class("stats");

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  //createCanvas(640, 360);

  target = "To be or not to be.";

  popmax = 200;

  mutationRate = 0.01;

​

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

  population = new Population(target, mutationRate, popmax);

}

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function draw() {

  // Generate mating pool

  population.naturalSelection();

  //Create next generation

  population.generate();

  // Calculate fitness

  population.calcFitness();

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  population.evaluate();

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  // If we found the target phrase, stop

  if (population.isFinished()) {

    //println(millis()/1000.0);

    noLoop();

  }

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  displayInfo();

}

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function displayInfo() {

  // Display current status of population

  let answer = population.getBest();

​

  bestPhrase.html("Best phrase:<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) + "%";

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  stats.html(statstext);

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  allPhrases.html("All phrases:<br>" + population.allPhrases());

}

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