Class: MetaheuristicAlgorithms::GeneticAlgorithm

Inherits:
Object
  • Object
show all
Includes:
BaseAlgorithmModule, Helper
Defined in:
lib/metaheuristic_algorithms/genetic_algorithm.rb

Instance Method Summary collapse

Methods included from Helper

#bigdecimal_rand

Methods included from BaseAlgorithmModule

#get_decision_variable_value_by_randomization

Constructor Details

#initialize(function_wrapper, number_of_variables: 1, objective: :maximization) ⇒ GeneticAlgorithm

Returns a new instance of GeneticAlgorithm.



7
8
9
10
11
12
13
14
15
16
17
18
19
20
 
# File 'lib/metaheuristic_algorithms/genetic_algorithm.rb', line 7

def initialize(function_wrapper, number_of_variables: 1, objective: :maximization)
  @function_wrapper = function_wrapper
  @number_of_variables = number_of_variables
  @objective_method_name = case objective
                             when :maximization
                               :max
                             when :minimization
                               :min
                           end        

  ## Decided to use decimal number representation and convert it to binary number by unpack method
  ## because it is difficult to initialize variable within the given range
  # @string_length_in_bits = 16
end

Instance Method Details

#search(population_size: 20, maximum_number_of_generations: 100, number_of_mutation_sites: BigDecimal('2'), crossover_probability: BigDecimal('0.95'), mutation_probability: BigDecimal('0.05')) ⇒ Object 



22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
 
# File 'lib/metaheuristic_algorithms/genetic_algorithm.rb', line 22

def search(population_size: 20, maximum_number_of_generations: 100, number_of_mutation_sites: BigDecimal('2'), crossover_probability: BigDecimal('0.95'), mutation_probability: BigDecimal('0.05'))

  population_size = population_size.to_i unless population_size.kind_of?(Integer)
  maximum_number_of_generations = maximum_number_of_generations.to_i unless maximum_number_of_generations.kind_of?(Integer)
  number_of_mutation_sites = BigDecimal(number_of_mutation_sites.to_s) unless number_of_mutation_sites.kind_of?(BigDecimal)
  crossover_probability = BigDecimal(crossover_probability.to_s) unless crossover_probability.kind_of?(BigDecimal)
  mutation_probability = BigDecimal(mutation_probability.to_s) unless mutation_probability.kind_of?(BigDecimal)

  initialize_population(population_size)

  (0...maximum_number_of_generations).each do |generation_index|

    @population_copy = deep_clone_population

    (0...population_size).each do |individual_index|

      if bigdecimal_rand < crossover_probability

        # Crossover pair:
        crossover_pair_1_index = generate_random_index(population_size)
        crossover_pair_2_index = generate_random_index(population_size)     

        crossover(crossover_pair_1_index, crossover_pair_2_index)

      end

      if bigdecimal_rand < mutation_probability

        mutation_individual_index = generate_random_index(population_size)

        mutate(mutation_individual_index, number_of_mutation_sites)

      end

    end

  end

  objective_function_value = @population_fitness.send(@objective_method_name)
  decision_variable_values = @population[@population_fitness.index(objective_function_value)]

  { best_decision_variable_values: decision_variable_values, best_objective_function_value: objective_function_value }

end