code: def eaGenerateUpdate(
      toolbox, ngen, halloffame=None, stats=None, verbose=__debug__
  ):
      """This is algorithm implements the ask-tell model proposed in
      [Colette2010]_, where ask is called `generate` and tell is called `update`.
  
      :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                      operators.
      :param ngen: The number of generation.
      :param stats: A :class:`~deap.tools.Statistics` object that is updated
                    inplace, optional.
      :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
                         contain the best individuals, optional.
      :param verbose: Whether or not to log the statistics.
      :returns: The final population
      :returns: A class:`~deap.tools.Logbook` with the statistics of the
                evolution
  
      The algorithm generates the individuals using the :func:`toolbox.generate`
      function and updates the generation method with the :func:`toolbox.update`
      function. It returns the optimized population and a
      :class:`~deap.tools.Logbook` with the statistics of the evolution. The
      logbook will contain the generation number, the number of evaluations for
      each generation and the statistics if a :class:`~deap.tools.Statistics` is
      given as argument. The pseudocode goes as follow ::
  
          for g in range(ngen):
              population = toolbox.generate()
              evaluate(population)
              toolbox.update(population)
  
  
      This function expects :meth:`toolbox.generate` and :meth:`toolbox.evaluate` aliases to be
      registered in the toolbox.
  
      .. [Colette2010] Collette, Y., N. Hansen, G. Pujol, D. Salazar Aponte and
         R. Le Riche (2010). On Object-Oriented Programming of Optimizers -
         Examples in Scilab. In P. Breitkopf and R. F. Coelho, eds.:
         Multidisciplinary Design Optimization in Computational Mechanics,
         Wiley, pp. 527-565;
  
      """
      logbook = tools.Logbook()
      logbook.header = ["gen", "nevals"] + (
          stats.fields if stats else []
      )
  
      for gen in range(ngen):
          # Generate a new population
          population = toolbox.generate()
          # Evaluate the individuals
          fitnesses = toolbox.map(toolbox.evaluate, population)
          for ind, fit in zip(population, fitnesses):
              ind.fitness.values = fit
  
          if halloffame is not None:
              halloffame.update(population)
  
          # Update the strategy with the evaluated individuals
          toolbox.update(population)
  
          record = (
              stats.compile(population) if stats is not None else {}
          )
          logbook.record(gen=gen, nevals=len(population), **record)
          if verbose:
              print(logbook.stream)
  
      return population, logbook
  
file_path: deap/deap/algorithms.py
imports: import random
  from .None import tools
label_desc: Use image node_id 6 for calling a global function with example usage: eaGenerateUpdate(toolbox, ngen, halloffame, stats, verbose) and returns: population,  logbook