feat: add number of constraints as a generic

feat: add possibility to map EvolutionResult evaluations

chore: remove Copy from requirement of TournamentSelection

refactor(lib): Allow fitting whole population at once

The fitness function gets fit_population function. The default
implementation is going over fit() one by one, but it can be
reimplemented by specific types.

feat(lib): constraints along with evolutionary strategies

chore: fix warnings

feat(lib): allow modifying fitness function in evolution_algorithm

chore: move population structs to separate module

feat: split params out of PerturbationOperator to allow for non-'static PerturbationOperator

This means PerturbationOperators can now hold non-'static references.
Right away, utilize this in the Random2OptPerturbation for the TSPInstance.

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