text: import abc
  from typing import List, Tuple
  from abc import abstractmethod
  
  from gp_framework.FitnessCalculator import FitnessCalculator
  from gp_framework.Genotype import Genotype
  from gp_framework.Genotype import PhenotypeConverter
  
  
  class LifecycleReport:
      """
      It's a POJO for whatever data we think is good to keep track of from generation to generation
      """
      def __init__(self, max_fitness=-1.0, min_fitness=-1.0, mean_fitness=-1.0, solution_found=False):
          self._max_fitness = max_fitness
          self._min_fitness = min_fitness
          self._mean_fitness = mean_fitness
          self._solution_found = solution_found
  
      def to_list(self):
          return [self.max_fitness, self.min_fitness, self.mean_fitness]
  
      @staticmethod
      def header() -> List[str]:
          return ['max_fitness', 'min_fitness', 'mean_fitness']
  
      @property
      def max_fitness(self):
          return self._max_fitness
  
      @property
      def min_fitness(self):
          return self._min_fitness
  
      @property
      def mean_fitness(self):
          return self._mean_fitness
  
      @property
      def solution_found(self):
          return self._solution_found
  
  
  class PopulationManager(abc.ABC):
      """
      This abstract class wraps up useful default behavior for searching the solution space.
      Subclass and override the behavior in produce_offspring and select_next_generation. Then, just call lifecycle.
      """
      def __init__(self, population: List[Genotype],
                   phenotype_converter: PhenotypeConverter,
                   fitness_calculator: FitnessCalculator):
          """
          todo: should M = len(population)?
          :param population: The starting population
          :param phenotype_converter: Closely related to the provided fitness
          calculator, this converts the provided genotype to a phenotype accepted
          by the fitness_calculator.
          :param fitness_calculator: This is used to judge our solutions
          :param phenotype_converter: converts the Genotypes into Phenotypes for use by fitness_calculator
          """
          self._population = population
          self._fitness_calculator = fitness_calculator
          self._phenotype_converter = phenotype_converter
          # this should be set in produce_offspring or select_next_generation and is returned by lifecycle
          self._newest_report: LifecycleReport = LifecycleReport()
  
      def calculate_population_fitness(self, population: List[Genotype])\
              -> Tuple[List[Tuple[Genotype, float]], LifecycleReport]:
          """
          Use fitness_calculator to assign a rank to each Genotype in the population
          :return: each member of the population with their fitness, a summary of important findings
          """
          max_fitness = -1
          min_fitness = -1
          total_fitness = 0
          judged_population = []
  
          for genotype in population:
              phenotype = self._phenotype_converter.convert(genotype)
              fitness = self._fitness_calculator.calculate_fitness(phenotype)
              judged_population.append((genotype, fitness))
              total_fitness += fitness
              if fitness > max_fitness:
                  max_fitness = fitness
              if min_fitness < 0 or fitness < min_fitness:
                  min_fitness = fitness
  
          report = LifecycleReport(max_fitness, min_fitness, total_fitness / len(judged_population), max_fitness == 1.0)
          return judged_population, report
  
      @abstractmethod
      def produce_offspring(self, population: List[Genotype]) -> Tuple[List[Genotype], List[Genotype]]:
          """
          Create new Genotypes from the old ones
          :param population: A list of tuples pairing a Genotype with its fitness
          :return: The parents (selected from population) who created the offspring, The offspring of population
          """
          pass
  
      @abstractmethod
      def select_next_generation(self, parents: List[Genotype], children: List[Genotype]) -> List[Genotype]:
          """
          Select M individuals to make up the next generation of Genotypes
          :param parents: The old generation paired with their fitnesses
          :param children: The offspring of parents
          :return: the new generation
          """
          pass
  
      def lifecycle(self) -> LifecycleReport:
          """
          Run the population of solutions through a selection process. self._population is not updated until the end
          of this method and can thus be accessed in produce_offspring and select_next_generation.
          :return: a summary of important findings
          """
  
          parents, children = self.produce_offspring(self._population)
          self._population = self.select_next_generation(parents, children)
          return self._newest_report
  