class SimpleGa::GeneticAlgorithm::Chromosome

A Chromosome is a representation of an individual solution for a specific problem. You will have to redifine the Chromosome representation for each particular problem, along with its fitness, mutate, reproduce, and seed methods.

Attributes

data[RW]
normalized_fitness[RW]

Public Class Methods

mutate(chromosome) click to toggle source

Mutation method is used to maintain genetic diversity from one generation of a population of chromosomes to the next. It is analogous to biological mutation.

The purpose of mutation in GAs is to allow the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other, thus slowing or even stopping evolution.

Callling the mutate function will “probably” slightly change a chromosome randomly.

# File lib/simple_ga/genetic_algorithm.rb, line 281
def self.mutate(chromosome)
  if chromosome.normalized_fitness && rand < ((1 - chromosome.normalized_fitness) * 0.4)
      courses, grades = [], []
      data = chromosome.data

      # Split the chromosome into 2.
      0.step(data.length-1, 2) do |j|
        courses << data[j]
        grades << data[j+1]
      end

      p1 = (rand * courses.length-1).ceil
      courses[p1] = rand(2)
      p2 = (rand * grades.length-1).ceil
      grades[p2] = (1 + rand(6))

      # Recombine the chromosome.
      # [0,1,2,3,4,5,6,7,8,9,10]
      0.upto(courses.length-1) do |j|
        even_index = j * 2
        odd_index =  even_index + 1
        data[even_index] = courses[j]
        data[odd_index] = grades[j]
      end

      chromosome.data = data
      @fitness = nil
  end
end
new(data) click to toggle source
# File lib/simple_ga/genetic_algorithm.rb, line 176
def initialize(data) # Chromosome.new
  @data = data
end
reproduce(a, b) click to toggle source

Chromosome a and b might be the same.

NOTE

Why is chromosome a the same as chromosome b?

# File lib/simple_ga/genetic_algorithm.rb, line 322
def self.reproduce(a, b)
  # We know that (a.data.length == b.data.length) must be always true.
  chromosomeLength = a.data.length
  aCourses, bCourses, aGrades, bGrades = [], [], [], []    

  # Split the chromosome into 2.
  0.step(chromosomeLength-1, 2) do |index|
    next_index = index + 1
    aCourses << a.data[index]
    aGrades << a.data[next_index]
    bCourses << b.data[index]
    bGrades << b.data[next_index]
  end

  # One-point crossover
  # We know that (aCourses.length == aGrades.length) must be always true.
  # However, we want to cut and cross at different points for the
  # corresponding course and grade array.
  # Avoid slice(0,0) or slice (11,11). It brings no changes.
  course_Xpoint = rand(aCourses.length-1) + 1 
  grade_Xpoint = rand(aGrades.length-1) + 1
  new_Courses = aCourses.slice(0, course_Xpoint) + bCourses.slice(course_Xpoint, aCourses.length)
  new_Grades = aGrades.slice(0, grade_Xpoint) + bGrades.slice(grade_Xpoint, aGrades.length)
  
  spawn = []
  # We know that (new_Courses.length == new_Grades.length) must be always true.
  0.upto(new_Courses.length-1) do |i|
    spawn << new_Courses[i]
    spawn << new_Grades[i]
  end

  return Chromosome.new(spawn)
end
seed() click to toggle source

Initializes an individual solution (chromosome) for the initial population. Usually the chromosome is generated randomly, but you can use some problem domain knowledge, to generate a (probably) better initial solution.

# File lib/simple_ga/genetic_algorithm.rb, line 361
def self.seed
  # Current state inputs to be retrieved from the database.
  # ncourse = 11
  seed = []

  max_credits = 20
  if @@total_credits > max_credits
    1.step(@@ncourse*2, 2) do |j|
      seed << rand(2)
      seed << (1 + rand(6))
    end
  else
    1.step(@@ncourse*2, 2) do |j|
      seed << 1
      seed << (1 + rand(6))
    end
  end

  return Chromosome.new(seed)
end
set_params(available_courses, current_gpa, acum_ch) click to toggle source

available_courses is an array of arrays containing a list of available courses with the course information e.g. [[<course_name>, <course_ch], [<course_name>, <course_ch]]

# File lib/simple_ga/genetic_algorithm.rb, line 385
def self.set_params(available_courses, current_gpa, acum_ch)
  @@credits = []
  @@old_gpa = current_gpa
  @@old_total_credits = acum_ch
  @@old_cgpa = @@old_gpa/@@old_total_credits
  @@ncourse = available_courses.length
  available_courses.each do |course_info|
    @@credits << course_info[1]
  end
  @@total_credits = @@credits.inject(0, :+)
end

Public Instance Methods

fitness() click to toggle source

The fitness method quantifies the optimality of a solution (that is, a chromosome) in a genetic algorithm so that that particular chromosome may be ranked against all the other chromosomes.

Optimal chromosomes, or at least chromosomes which are more optimal, are allowed to breed and mix their datasets by any of several techniques, producing a new generation that will (hopefully) be even better.

# File lib/simple_ga/genetic_algorithm.rb, line 197
def fitness
  return @fitness if @fitness

  # Current state inputs to be retrieved from the database.
  # @@credits = [2,3,3,3,5,2,4,4,4,4,3]
  # @@old_gpa = 58
  # @@old_total_credits = 16
  min_credits = 16
  max_credits = 20
  target_cgpa ||= 3.7

  courses, grades, points = [], [], []
  # Split the chromosome into 2.
  0.step(@data.length-1, 2) do |j|
    courses << @data[j]
    grades << @data[j+1]
  end

  total_credits = ([courses, @@credits].transpose.map {|x| x.inject(:*)}).inject{|sum,x| sum + x }
  new_total_credits = @@old_total_credits + total_credits

  grades.each do |grade|
    case grade
      when 1
        points << 4.00
      when 2
        points << 3.70
      when 3
        points << 3.30
      when 4
        points << 3.00
      when 5
        points << 2.70
      when 6
        points << 2.30
      when 7
        points << 2.00
      else 
        points << 0.00
    end
  end

  gpa = (([@@credits, courses, points].transpose.map {|x| x.inject(:*)}).inject{|sum,x| sum + x }).round(2)
  new_gpa = @@old_gpa + gpa
  cgpa = (new_gpa/new_total_credits).round(2)

  # Core constraints.
  # Elites own a high fitness value.
  # The higher the fitness value, the higher the chance of being selected.
  if total_credits <= max_credits && total_credits >= min_credits
      @fitness = cgpa
  # This chromosome doesn't satisfy the important constraints
  # e.g. within the range of min_credits and max_credits
  # should be discarded.

  # Perhaps, we should add cases that statisfy one of the constraints
  # 1. credit hours + cgpa
  # 2. credit hours
  else 
      # Negative cgpa value will allow the invalid chromosome to be considered.
      # However, surprisingly the solutions suggested are more consistent and
      # diverse.
      @fitness = 0
  end
  return @fitness
end
improved_fitness() click to toggle source

The evolution value

# File lib/simple_ga/genetic_algorithm.rb, line 265
def improved_fitness
  return @fitness - @@old_cgpa
end
num_of_selected_genes() click to toggle source
# File lib/simple_ga/genetic_algorithm.rb, line 180
def num_of_selected_genes
  count = 0
  data.each_with_index do |gene, index|
    if index % 2 == 0 && gene == 1
      count += 1
    end
  end
  return count
end