code: def main():
      pop = toolbox.population(n=50)
      CXPB, MUTPB, NGEN = 0.5, 0.2, 40
  
      # Evaluate the entire population
      fitnesses = map(toolbox.evaluate, pop)
      for ind, fit in zip(pop, fitnesses):
          ind.fitness.values = fit
  
      for g in range(NGEN):
          # Select the next generation individuals
          offspring = toolbox.select(pop, len(pop))
          # Clone the selected individuals
          offspring = map(toolbox.clone, offspring)
  
          # Apply crossover and mutation on the offspring
          for child1, child2 in zip(offspring[::2], offspring[1::2]):
              if random.random() < CXPB:
                  toolbox.mate(child1, child2)
                  del child1.fitness.values
                  del child2.fitness.values
  
          for mutant in offspring:
              if random.random() < MUTPB:
                  toolbox.mutate(mutant)
                  del mutant.fitness.values
  
          # Evaluate the individuals with an invalid fitness
          invalid_ind = [
              ind for ind in offspring if not ind.fitness.valid
          ]
          fitnesses = map(toolbox.evaluate, invalid_ind)
          for ind, fit in zip(invalid_ind, fitnesses):
              ind.fitness.values = fit
  
          # The population is entirely replaced by the offspring
          pop[:] = offspring
  
      return pop
  
imports: from deap import base, creator
  import random
  from deap import tools
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: pop