feat: add convenient function for stochastic ranking evaluation

refactor: add possibility to use different logic for obtaining best candidate

fix(eoa_lib): extract random distribution parameter out of perturbation

feat: add multi objective; NSGA

feat: add stochastic ranking

feat: add equal to BetterThan operator

feat: add multiple weights to constrained functions

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