feat(tsp): add MinimalSpanningTree initializer

feat(tsp): add 2opt perturbation for subsequence reversal

feat(tsp): add TSPInstance convertor to GenericGraph

feat(tsp): add minimal spanning tree and filter_edges

feat(tsp): add nearest neighbor initializer

feat(tsp): add cycle, no and partially mapped crossovers

refactor(tsp): split to multiple files out of tsp.rs

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

feat(lib): add roulette wheel selection

refactor(tsp): put plotting to main to not repeat code

feat(lib): add random search

feat(tsp): add ea binary algorithm

tests(tsp): adapt to new binarystring -> node permutation implementation

tests(tsp): add test for verify_solution

feat(tsp): add move node perturbation

fix(tsp): properly implement binary string -> node permutation fitness

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(tsp): allow crossing bounds in reverse subsequence perturbation

Currently subsequence perturbation was capable only of changes
inside of bounds (left, right), but allow it going through
the right bound, ie. from element 8 to element 1 - swapping
8 with 1, 9 with 0, 10 with 10... this generalizes the perturbation.
Also, change the behavior to generate an index and a length instead
of two indices. This allows for constraining the length of
subsequences.