ASRC GOES 7:00 – 5:00

• Dissertation. Working on the differences between designed and evolved systems
• Status report
• Add statistical tests to the evolver.
  • based on this post, starting with the scikit-learn resample(). Here are the important bits:

    def calc_fitness_stats(self, resample_size:int = 100):
        boot = resample(self.population, replace=True, n_samples=resample_size, random_state=1)
        s = pd.Series(boot)
        conf = st.t.interval(0.95, len(boot)-1, loc=s.mean(), scale= st.sem(boot))
        self.meta_info = {'mean':s.mean(), '5_conf':conf[0], '95_conf':conf[1], 'max':s.max(), 'min':s.min()}
        self.fitness = s.mean()

  • And the convergence on the test landscape looks good:

• Added check that the same genome doesn’t get re-run, since it will be run for n times to produce a distribution:

  # randomly breed new genomes with a chance of mutation
  while len(self.current_genome_list) < self.num_genomes:
      g1i = random.randrange(len(self.best_genome_list))
      g2i = random.randrange(len(self.best_genome_list))
      g1 = self.best_genome_list[g1i]
      g2 = self.best_genome_list[g2i]
      g = self.breed_genomes(g1, g2, crossover_rate, mutation_rate)
      match = False
      for gtest in self.all_genomes_list:
          if g.chromosome_dict == gtest.chromosome_dict:
              match = True
              break
      if not match:
          self.current_genome_list.append(g)
          self.all_genomes_list.append(g)