Implemented basic functionality for using different target fitness types

src/run_testing.py

@@ -3,10 +3,22 @@ import EasyGA
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
+ga.target_fitness_type = 'min'
+ga.chromosome_length = 10
+ga.population_size = 25
+ga.generation_goal = 50
+ga.gene_impl = lambda: random.randint(0, 10)
 
-ga.chromosome_length = 100
+def fitness_function(chromosome):
+    return sum(
+               gene.get_value()
+           for gene in chromosome.get_gene_list())
+
+ga.fitness_function_impl = fitness_function
 
 ga.evolve()
+ga.set_all_fitness()
+ga.population.sort_by_best_fitness(ga)
 
 print(f"Current Generation: {ga.current_generation}")
 ga.population.print_all()