Added more structure methods and some quality of life changes

Overall cleaned up a lot of comments.

EasyGA:
- Code cleanup.

Population:
- Added sort_by_best_fitness
- Added parent/mating pool methods.
- Renamed some methods for consistency.

Chromosome:
- Added get_gene(index).

Parent Selection:
- Improved selection methods to use the ga.selection_probability so that the roulette selection actually works well.
- Added stochastic selection.

Survivor Selection:
- Added fill_in_random and fill_in_parents_then_random.

Crossover/Mutation:
- Cleaned up code.

src/run_testing.py

@@ -3,8 +3,15 @@ import EasyGA
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
+ga.population_size = 100
+ga.generation_goal = 200
+ga.parent_selection_impl     = EasyGA.Parent_Selection.Roulette.stochastic_selection
+ga.crossover_population_impl = EasyGA.Crossover_Methods.Population.sequential_selection
+ga.survivor_selection_impl   = EasyGA.Survivor_Selection.fill_in_parents_then_random
 
 ga.evolve()
+ga.set_all_fitness()
+ga.population.set_all_chromosomes(ga.sort_by_best_fitness(ga.population.get_all_chromosomes()))
 
 print(f"Current Generation: {ga.current_generation}")
 ga.population.print_all()