Method: Ai4r::GeneticAlgorithm::GeneticSearch#run

Defined in:
lib/ai4r/genetic_algorithm/genetic_algorithm.rb

#run ⇒ Object

  1. Choose initial population

    2. Evaluate the fitness of each individual in the population
3. Repeat
      1. Select best-ranking individuals to reproduce
      2. Breed new generation through crossover and mutation (genetic operations) and give birth to offspring
      3. Evaluate the individual fitnesses of the offspring
      4. Replace worst ranked part of population with offspring
4. Until termination
5. Return the best chromosome

Returns:

  • (Object) 


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# File 'lib/ai4r/genetic_algorithm/genetic_algorithm.rb', line 64

def run
  generate_initial_population                    # Generate initial population
  best = best_chromosome
  best_fitness = best.fitness
  stagnation = 0
  @on_generation&.call(@generation, best_fitness)
  @max_generation.times do
    @generation += 1
    selected_to_breed = selection                # Evaluates current population
    offsprings = reproduction selected_to_breed  # Generate the population for this new generation
    replace_worst_ranked offsprings
    current_best = best_chromosome
    if current_best.fitness > best_fitness
      best_fitness = current_best.fitness
      best = current_best
      stagnation = 0
    else
      stagnation += 1
    end
    @on_generation&.call(@generation, best_fitness)
    break if (@fitness_threshold && best_fitness >= @fitness_threshold) ||
             (@max_stagnation && stagnation >= @max_stagnation)
  end
  best
end