min_max_select.py

def min_max_select(constraints, population_in, features_names, generation_size, population_out_size, target, model):
  
  # We create a new generation, based on the input population characteristics
  new_generation = generate_min_max_population(population_in, constraints, generation_size)

  # We append the original population to the new generation to keep the best
  # individuals of these two DataFrames
  new_generation = new_generation.append(population_in, ignore_index=True)
  
  # We calculate Y thanks to the model and the distance from target
  new_generation["Y"]= model.predict(new_generation)
  new_generation["target_distance"]=abs(new_generation["Y"]-np.ones(new_generation.shape[0])*target)
  
  # We sort individuals according to their distance from the target and
  # keep only the desired number of individuals
  new_generation = new_generation.sort_values(by="target_distance").head(population_out_size)

  return new_generation