feat: add a simple python plotter

fix: properly evaluate nsga population

The full population has to be evaluated at once,
offsprings cannot be evaluated individually.

feat: implement improved nsga for constraints with archive of feasible solutions

feat: implement constrained nsga

Lots of changes I lost track of

fix: implement possibility of best candidate not being found

fi: stochastic ranking properly compares constraints only

feat: add bounded crossover similar to bounded perturbation

fix: constraint for ArithmeticCrossover

feat: Add BlendCrossover for reals

feat: add ArithmeticCrossover for reals

fix: make sure both parents are used for ovector one point

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