feat: add a lot of algorithm showcases

feat(lib): add evaluation count to EA stats

fix(lib): do not take mut self in population.iter()

feat(lib): add roulette wheel selection

feat(lib): add random search

feat(lib): add n consecutive bit flip perturbation for binary string

feat(lib): add two point and n point crossovers

refactor(lib): implement generic evolutionary strategy using .wrapped_mut

Thanks to wrapped_mut now the one_to_five strategy can be implemented
without relying on BoundedPerturbation etc.

feat: add possibility to evolve strategy during evolution_algorithm

refactor: generalize apply_to_mutations

refactor(lib): allow modifying mutation perturbation

refactor(lib): add methods to get wrapped perturbations instead of traits.

Remove WrapperPerturbation and ListWrapperPerturbation in favor of
wrapped and wrapped_mut functions on PerturbationOperator. This
allows for easier handling as it's not easy to downcast Any into
WrapperPerturbation or ListWrapperPerturbation. So instead
always return dyn PerturbationOperator instead of dyn Any.
Use dyn Any only for downstasting to concrete types.

This finally allows for apply_to_mutations to be implemented
under MutationPerturbation, allowing to modify the arguments.

chore: do not clone for no reason in local search

feat: add into_iter for population

feat: add utilities to perturbation to iterate through wrappers easily

Make PerturbationOperator implement Any, allowing it to be downcasted.
Make WrapperPerturbation for perturbations that wrap other
perturbations, such as the mutation perturbation and bounded perturbation.
Make ListWrapperPerturbation for pertrubations that wrap multiple
pertrubations, such as the combined perturbation.

This should allow for easily looking through perturbations, finding
all perturbations of a specific type and modifying them. One use
case can be for changing the probability of a mutation perturbation
during evolution.

chore: do not pass pairing as mut

tests: tweak one_max parameters

refactor: abstract pairing to n-ary

tests: adjust evolution one_max algorithm to always find optimum

feat(binary_string): add single bit perturbation, flip perturbation