the main loop from my python example for my blog, source available: https://github.com/topher200/genetic-hello-world-python

exmaple_from_genetic_hello_world.py

  def run(self):
    # Create a random sample of chromos
    sample = self.generate_random_chromosomes()

    # Main loop: each generation select a subset of the sample and breed from
    # them.
    generation = -1
    while self.fitness(sample[0]) != 0:
      generation += 1
      # Generate the selected group from sample- take the top 1% of samples
      # and tourny select to generate the rest of selected.
      ten_percent = int(len(sample)*.01)
      selected = sample[:ten_percent]
      while len(selected) < self.num_selected:
        selected.append(self.tourny_select_chromo(sample))

      # Generate the solution group by breeding random chromos from selected
      solution = []
      while len(solution) < self.num_samples:
        solution.extend(self.breed(random.choice(selected),
                                   random.choice(selected)))

      # Apply a mutation to a subset of the solution set
      for i, chromo in enumerate(solution[::self.mutation_factor]):
        solution[i] = self.mutate(solution[i])

      sample = sorted(solution, key = self.fitness)
      # Print useful stats about this generation
      (min, median, max) = map(self.fitness,
                               [sample[0], sample[len(sample)//2], sample[-1]])
      print("{0} best string: {1}. fitness: best {2}, median {3}, worst {4}" \
              .format(generation, sample[0], min, median, max))

    return generation