Class: SimpleGa::GeneticAlgorithm::Chromosome

Inherits:
Object
  • Object
show all
Defined in:
lib/simple_ga/genetic_algorithm.rb

Overview

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.

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(data) ⇒ Chromosome

Chromosome.new


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# File 'lib/simple_ga/genetic_algorithm.rb', line 175

def initialize(data) # Chromosome.new
  @data = data
end

Instance Attribute Details

#data ⇒ Object

Returns the value of attribute data.


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# File 'lib/simple_ga/genetic_algorithm.rb', line 172

def data
  @data
end

#normalized_fitness ⇒ Object

Returns the value of attribute normalized_fitness.


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# File 'lib/simple_ga/genetic_algorithm.rb', line 173

def normalized_fitness
  @normalized_fitness
end

Class Method Details

.mutate(chromosome) ⇒ Object

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.


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# File 'lib/simple_ga/genetic_algorithm.rb', line 278

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

.reproduce(a, b) ⇒ Object

Chromosome a and b might be the same.

NOTE

Why is chromosome a the same as chromosome b?


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# File 'lib/simple_ga/genetic_algorithm.rb', line 319

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 ⇒ Object

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.


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# File 'lib/simple_ga/genetic_algorithm.rb', line 358

def self.seed
  # Current state inputs to be retrieved from the database.
  # ncourse = 11
  seed = []

  1.step(@@ncourse*2, 2) do |j|
    seed << rand(2)
    seed << (1 + rand(6))
  end
  return Chromosome.new(seed)
end

.set_params(available_courses, current_gpa, acum_ch) ⇒ Object

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]]


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# File 'lib/simple_ga/genetic_algorithm.rb', line 373

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
end

Instance Method Details

#fitness ⇒ Object

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.


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# File 'lib/simple_ga/genetic_algorithm.rb', line 196

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
    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 && cgpa >= target_cgpa
      @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 ⇒ Object

The evolution value


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# File 'lib/simple_ga/genetic_algorithm.rb', line 262

def improved_fitness
  return @fitness - @@old_cgpa
end

#num_of_selected_genes ⇒ Object 


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# File 'lib/simple_ga/genetic_algorithm.rb', line 179

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