test/genetic_algorithm.rb
def selection()
        acum_fitness = 0

        fitness_range = get_fitness_range(@population)

        if fitness_range.max - fitness_range.min > 0
          @population.each do |chromosome| 
            acum_fitness += chromosome.get_normalized_fitness(fitness_range)
          end
        end

        # selected_to_breed
        @breed_count.times.map { select_random_individual(acum_fitness, fitness_range) }
      end
# Select best-ranking individuals to reproduce
# 
# Selection is the stage of a genetic algorithm in which individual 
# genomes are chosen from a population for later breeding. 
# There are several generic selection algorithms, such as 
# tournament selection and roulette wheel selection. We implemented the
# latest.
# 
# Steps:
# 
# 1. The fitness function is evaluated for each individual, providing fitness values
# 2. The population is sorted by descending fitness values.
# 3. The fitness values ar then normalized. (Highest fitness gets 1, lowest fitness gets 0). The normalized value is stored in the "normalized_fitness" attribute of the chromosomes.
# 4. A random number R is chosen. R is between 0 and the accumulated normalized value (all the normalized fitness values added togheter).
# 5. The selected individual is the first one whose accumulated normalized value (its is normalized value plus the normalized values of the chromosomes prior it) greater than R.
# 6. We repeat steps 4 and 5, 2/3 times the population size.    
Select best-ranking individuals to reproduce
Selection is the stage of a genetic algorithm in which individual
genomes are chosen from a population for later breeding.
There are several generic selection algorithms, such as
tournament selection and roulette wheel selection. We implemented the
latest.



1. The fitness function is evaluated for each individual, providing fitness values
2. The population is sorted by descending fitness values.
3. The fitness values ar then normalized. (Highest fitness gets 1, lowest fitness gets 0). The normalized value is stored in the "normalized_fitness" attribute of the chromosomes.
4. A random number R is chosen. R is between 0 and the accumulated normalized value (all the normalized fitness values added togheter).
5. The selected individual is the first one whose accumulated normalized value (its is normalized value plus the normalized values of the chromosomes prior it) greater than R.
6. We repeat steps 4 and 5, 2/3 times the population size.
Select best - ranking individuals to reproduce Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding . There are several generic selection algorithms such as tournament selection and roulette wheel selection . We implemented the latest . 1 . The fitness function is evaluated for each individual providing fitness values 2 . The population is sorted by descending fitness values . 3 . The fitness values ar then normalized . ( Highest fitness gets 1 lowest fitness gets 0 ) . The normalized value is stored in the " normalized_fitness " attribute of the chromosomes . 4 . A random number R is chosen . R is between 0 and the accumulated normalized value ( all the normalized fitness values added togheter ) . 5 . The selected individual is the first one whose accumulated normalized value ( its is normalized value plus the normalized values of the chromosomes prior it ) greater than R . 6 . We repeat steps 4 and 5 2 / 3 times the population size .
def selection()
        acum_fitness = 0
        fitness_range = get_fitness_range(@population)
        if fitness_range.max - fitness_range.min > 0
          @population.each do |chromosome| 
            acum_fitness += chromosome.get_normalized_fitness(fitness_range)
          end
        end
        @breed_count.times.map { select_random_individual(acum_fitness, fitness_range) }
      end
def selection ( ) acum_fitness = 0 fitness_range = get_fitness_range ( @population ) if fitness_range . max - fitness_range . min > 0 @population . each do | chromosome | acum_fitness += chromosome . get_normalized_fitness ( fitness_range ) end end @breed_count . times . map { select_random_individual ( acum_fitness , fitness_range ) } end
Select best-ranking individuals to reproduce
Selection is the stage of a genetic algorithm in which individual
genomes are chosen from a population for later breeding.
Select best - ranking individuals to reproduce Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding .
# selected_to_breed