neataptic/src/neat.js

Architecture-free neural network library with genetic algorithm implementations

/* Export */
module.exports = Neat;

/* Import */
var Network = require('./architecture/network');
var methods = require('./methods/methods');
var config = require('./config');

/* Easier variable naming */
var selection = methods.selection;

/*******************************************************************************
                                         NEAT
*******************************************************************************/

function Neat (input, output, fitness, options) {
  this.input = input; // The input size of the networks
  this.output = output; // The output size of the networks
  this.fitness = fitness; // The fitness function to evaluate the networks

  // Configure options
  options = options || {};
  this.equal = options.equal || false;
  this.clear = options.clear || false;
  this.popsize = options.popsize || 50;
  this.elitism = options.elitism || 0;
  this.provenance = options.provenance || 0;
  this.mutationRate = options.mutationRate || 0.3;
  this.mutationAmount = options.mutationAmount || 1;

  this.fitnessPopulation = options.fitnessPopulation || false;

  this.selection = options.selection || methods.selection.POWER;
  this.crossover = options.crossover || [
    methods.crossover.SINGLE_POINT,
    methods.crossover.TWO_POINT,
    methods.crossover.UNIFORM,
    methods.crossover.AVERAGE
  ];
  this.mutation = options.mutation || methods.mutation.FFW;

  this.template = options.network || false;

  this.maxNodes = options.maxNodes || Infinity;
  this.maxConns = options.maxConns || Infinity;
  this.maxGates = options.maxGates || Infinity;

  // Custom mutation selection function if given
  this.selectMutationMethod = typeof options.mutationSelection === 'function' ? options.mutationSelection.bind(this) : this.selectMutationMethod;

  // Generation counter
  this.generation = 0;

  // Initialise the genomes
  this.createPool(this.template);
}

Neat.prototype = {
  /**
   * Create the initial pool of genomes
   */
  createPool: function (network) {
    this.population = [];

    for (var i = 0; i < this.popsize; i++) {
      var copy;
      if (this.template) {
        copy = Network.fromJSON(network.toJSON());
      } else {
        copy = new Network(this.input, this.output);
      }
      copy.score = undefined;
      this.population.push(copy);
    }
  },

  /**
   * Evaluates, selects, breeds and mutates population
   */
  evolve: async function () {
    // Check if evaluated, sort the population
    if (typeof this.population[this.population.length - 1].score === 'undefined') {
      await this.evaluate();
    }
    this.sort();

    var fittest = Network.fromJSON(this.population[0].toJSON());
    fittest.score = this.population[0].score;

    var newPopulation = [];

    // Elitism
    var elitists = [];
    for (var i = 0; i < this.elitism; i++) {
      elitists.push(this.population[i]);
    }

    // Provenance
    for (i = 0; i < this.provenance; i++) {
      newPopulation.push(Network.fromJSON(this.template.toJSON()));
    }

    // Breed the next individuals
    for (i = 0; i < this.popsize - this.elitism - this.provenance; i++) {
      newPopulation.push(this.getOffspring());
    }

    // Replace the old population with the new population
    this.population = newPopulation;
    this.mutate();

    this.population.push(...elitists);

    // Reset the scores
    for (i = 0; i < this.population.length; i++) {
      this.population[i].score = undefined;
    }

    this.generation++;

    return fittest;
  },

  /**
   * Breeds two parents into an offspring, population MUST be surted
   */
  getOffspring: function () {
    var parent1 = this.getParent();
    var parent2 = this.getParent();

    return Network.crossOver(parent1, parent2, this.equal);
  },

  /**
   * Selects a random mutation method for a genome according to the parameters
   */
  selectMutationMethod: function (genome) {
    var mutationMethod = this.mutation[Math.floor(Math.random() * this.mutation.length)];

    if (mutationMethod === methods.mutation.ADD_NODE && genome.nodes.length >= this.maxNodes) {
      if (config.warnings) console.warn('maxNodes exceeded!');
      return;
    }

    if (mutationMethod === methods.mutation.ADD_CONN && genome.connections.length >= this.maxConns) {
      if (config.warnings) console.warn('maxConns exceeded!');
      return;
    }

    if (mutationMethod === methods.mutation.ADD_GATE && genome.gates.length >= this.maxGates) {
      if (config.warnings) console.warn('maxGates exceeded!');
      return;
    }

    return mutationMethod;
  },

  /**
   * Mutates the given (or current) population
   */
  mutate: function () {
    // Elitist genomes should not be included
    for (var i = 0; i < this.population.length; i++) {
      if (Math.random() <= this.mutationRate) {
        for (var j = 0; j < this.mutationAmount; j++) {
          var mutationMethod = this.selectMutationMethod(this.population[i]);
          this.population[i].mutate(mutationMethod);
        }
      }
    }
  },

  /**
   * Evaluates the current population
   */
  evaluate: async function () {
    var i;
    if (this.fitnessPopulation) {
      if (this.clear) {
        for (i = 0; i < this.population.length; i++) {
          this.population[i].clear();
        }
      }
      await this.fitness(this.population);
    } else {
      for (i = 0; i < this.population.length; i++) {
        var genome = this.population[i];
        if (this.clear) genome.clear();
        genome.score = await this.fitness(genome);
      }
    }
  },

  /**
   * Sorts the population by score
   */
  sort: function () {
    this.population.sort(function (a, b) {
      return b.score - a.score;
    });
  },

  /**
   * Returns the fittest genome of the current population
   */
  getFittest: function () {
    // Check if evaluated
    if (typeof this.population[this.population.length - 1].score === 'undefined') {
      this.evaluate();
    }
    if (this.population[0].score < this.population[1].score) {
      this.sort();
    }

    return this.population[0];
  },

  /**
   * Returns the average fitness of the current population
   */
  getAverage: function () {
    if (typeof this.population[this.population.length - 1].score === 'undefined') {
      this.evaluate();
    }

    var score = 0;
    for (var i = 0; i < this.population.length; i++) {
      score += this.population[i].score;
    }

    return score / this.population.length;
  },

  /**
   * Gets a genome based on the selection function
   * @return {Network} genome
   */
  getParent: function () {
    var i;
    switch (this.selection) {
      case selection.POWER:
        if (this.population[0].score < this.population[1].score) this.sort();

        var index = Math.floor(Math.pow(Math.random(), this.selection.power) * this.population.length);
        return this.population[index];
      case selection.FITNESS_PROPORTIONATE:
        // As negative fitnesses are possible
        // https://stackoverflow.com/questions/16186686/genetic-algorithm-handling-negative-fitness-values
        // this is unnecessarily run for every individual, should be changed

        var totalFitness = 0;
        var minimalFitness = 0;
        for (i = 0; i < this.population.length; i++) {
          var score = this.population[i].score;
          minimalFitness = score < minimalFitness ? score : minimalFitness;
          totalFitness += score;
        }

        minimalFitness = Math.abs(minimalFitness);
        totalFitness += minimalFitness * this.population.length;

        var random = Math.random() * totalFitness;
        var value = 0;

        for (i = 0; i < this.population.length; i++) {
          let genome = this.population[i];
          value += genome.score + minimalFitness;
          if (random < value) return genome;
        }

        // if all scores equal, return random genome
        return this.population[Math.floor(Math.random() * this.population.length)];
      case selection.TOURNAMENT:
        if (this.selection.size > this.popsize) {
          throw new Error('Your tournament size should be lower than the population size, please change methods.selection.TOURNAMENT.size');
        }

        // Create a tournament
        var individuals = [];
        for (i = 0; i < this.selection.size; i++) {
          let random = this.population[Math.floor(Math.random() * this.population.length)];
          individuals.push(random);
        }

        // Sort the tournament individuals by score
        individuals.sort(function (a, b) {
          return b.score - a.score;
        });

        // Select an individual
        for (i = 0; i < this.selection.size; i++) {
          if (Math.random() < this.selection.probability || i === this.selection.size - 1) {
            return individuals[i];
          }
        }
    }
  },

  /**
   * Export the current population to a json object
   */
  export: function () {
    var json = [];
    for (var i = 0; i < this.population.length; i++) {
      var genome = this.population[i];
      json.push(genome.toJSON());
    }

    return json;
  },

  /**
   * Import population from a json object
   */
  import: function (json) {
    var population = [];
    for (var i = 0; i < json.length; i++) {
      var genome = json[i];
      population.push(Network.fromJSON(genome));
    }
    this.population = population;
    this.popsize = population.length;
  }
};