Changing population when using decorator meta-problem

(This should be under the feature-request/help labels, I'm just not sure how to add them)

I have written code that changes a problem using a decorator, but I'd also like to change the problem in an instantiated population. Is there currently a way to do this? For example, given some population with an associated problem at initialisation, is there a way to update the problem to the new decorated one without creating a new population? I know this is potentially very un-pygmonic (against the way PyGMO is designed), but I think it could be useful for some upcoming projects.

E.g.
```
import pygmo as pg

# Define a dummy decorated problem
def f_decor(orig_fitness_function):
def new_fitness_function(self, dv):
import time
start = time.monotonic()
fitness = orig_fitness_function(self, dv)
print("Elapsed time: {} seconds".format(time.monotonic() - start))
return fitness
return new_fitness_function

# The original problem
prob = pg.problem(pg.rosenbrock(dim = 10))

# The initial population
pop = pg.population(prob, size = 20)

# The algorithm
algo = pg.algorithm(pg.sade(gen = 1000))

# Evolve the population
pop = algo.evolve(pop)

new_prob = pg.problem(pg.decorator_problem(rb, fitness_decorator=f_decor))

#
pop = pop.change_problem(new_prob)
```

Any help is much appreciated!

Start with the pg.population and pg.problem entry points shown in the example, and trace how a population retains its associated problem through algo.evolve(). Check the existing public API for any supported problem-replacement operation. Done would require a clear decision and documented behavior for changing the problem without recreating the population.

Written by the indexing model from the issue text.