Develop mutation in the Generation class

[gnypit]

Based on the probability of reseting a member of the generation (prob=10% means we randomly choose 10% of the members and use the genome generator to create new genomes for selected members), an efficient way to apply mutation is required.

IMPORTANT: it shouldn't be done in a loop, as computing probabilities, even pseudo-random ones, takes up a lot of time. It's better to have a tool to create a "mask" with indexes of the members to be mutated and then, e.g., to use the mutate_member method in the Generation class.

[gnypit]

At this point a corresponding method is in the GeneticAlgorithm class, so it's probably too high-level than it should in the future for parallelisation purposes: https://github.com/gnypit/pyqkd/blob/af543841315080e49b7135e06a19019255745f62/genal/genetic_algorithm.py

def mutate(self):
        """Mutation probability is the probability of 'resetting' a member of the current generation, i.e. changing
        it genome randomly. For optimisation purposes instead of a loop over the whole generation, I calculate the
        number of members to be mutated and then generate pseudo-randomly a list of member indexes in the current
        generation to be mutated.
        """
        number_of_mutations = np.floor(self.mutation_prob * self.current_generation.size)

        """Size of generation is a constant, it has to be adjusted to the lack of elite; the elite Members are not
        supposed to be mutated. Additionally, number of mutations has to be an integer, e.g., 
        half of a mutation cannot be performed.
        """
        indexes = random.sample(
            range(self.current_generation.size - self.elite_size),
            int(number_of_mutations)  # has to be an integer, e.g. you can't make half of a mutation
        )

        """For new (mutated) genome creation I use the generator passed to the superclass in it's initialisation:"""
        for index in indexes:
            self.current_generation.members[index].change_genes(
                self.genome_generator(self.genome_generator_args)
            )