Moptipy

We now also have [fitness proportionate selection](https://thomasweise.github.io/moptipy/moptipy.algorithms.modules.selections.html#moptipy.algorithms.modules.selections.fitness_proportionate_sus.FitnessProportionateSUS) and much better documentation.

The class [`FullEA`](https://thomasweise.github.io/moptipy/moptipy.algorithms.so.html#moptipy.algorithms.so.full_ea.FullEA) has been added, which uses instances of [`Selection`](https://thomasweise.github.io/moptipy/moptipy.algorithms.modules.html#moptipy.algorithms.modules.selection.Selection) for both survival and mating selection as well as a [`Fitness`](https://thomasweise.github.io/moptipy/moptipy.algorithms.so.html#moptipy.algorithms.so.fitness.Fitness) assignment process.
This should allow to configure a very wide variety of Evolutionary Algorithms (EAs), ranging from classical Genetic Algorithms over [`(mu+lambda) EAs`](https://thomasweise.github.io/moptipy/moptipy.algorithms.so.html#moptipy.algorithms.so.ea.EA) to EAs using [`FFA`](https://thomasweise.github.io/moptipy/moptipy.algorithms.so.fitnesses.html#moptipy.algorithms.so.fitnesses.ffa.FFA) or local searches like [`RLS`](https://thomasweise.github.io/moptipy/moptipy.algorithms.so.html#moptipy.algorithms.so.rls.RLS).

The new nullary operator [`Op0Forward`](https://thomasweise.github.io/moptipy/moptipy.operators.html#moptipy.operators.op0_forward.Op0Forward) can be used in conjunction with the sub-process [`from_starting_point`](https://thomasweise.github.io/moptipy/moptipy.api.html#moptipy.api.subprocesses.from_starting_point) to turn normal optimization methods seeded algorithms that can be used as local searches or sub-algorithms in other procedures.

- End Result Tests (table with Mann-Whitney U test results)
- Faster Permutation Recombination (GAP operator)
- Better Number Formatting (easier to adapt, better handling of large integer)

EA Modified Again, Also Fitness-based EA Added

Hopefully this fixed the PiPy distribution upload error in the GitHub workflow.