diff --git a/src/crossover/crossover_methods.py b/src/crossover/crossover_methods.py
index dcde3a6..a8e2d57 100644
--- a/src/crossover/crossover_methods.py
+++ b/src/crossover/crossover_methods.py
@@ -72,7 +72,7 @@ class Crossover_Methods:
         def single_point(ga, parent_1, parent_2):
             """Cross two parents by swapping genes at one random point."""
 
-            swap_index = random.randint(0, len(parent_1)-1)
+            swap_index = random.randrange(len(parent_1))
             return parent_1[:swap_index] + parent_2[swap_index:]
 
 
diff --git a/src/mutation/mutation_methods.py b/src/mutation/mutation_methods.py
index 72819a4..d39b9e2 100644
--- a/src/mutation/mutation_methods.py
+++ b/src/mutation/mutation_methods.py
@@ -37,7 +37,7 @@ class Mutation_Methods:
         def random_selection(ga):
             """Selects random chromosomes."""
 
-            index = random.randint(0, len(ga.population)-1)
+            index = random.randrange(len(ga.population))
             ga.population[index] = ga.mutation_individual_impl(ga, ga.population[index])
 
 
@@ -45,7 +45,7 @@ class Mutation_Methods:
         def random_selection_then_cross(ga):
             """Selects random chromosomes and self-crosses with parent."""
 
-            index = random.randint(0, len(ga.population)-1)
+            index = random.randrange(len(ga.population))
             chromosome = ga.population[index]
             ga.population[index] = ga.crossover_individual_impl(ga, chromosome, ga.mutation_individual_impl(ga, chromosome))
 
@@ -56,7 +56,7 @@ class Mutation_Methods:
         @loop_mutations
         def individual_genes(ga, chromosome):
             """Mutates a random gene in the chromosome."""
-            index = random.randint(0, len(chromosome)-1)
+            index = random.randrange(len(chromosome))
 
             # Using the chromosome_impl
             if ga.chromosome_impl is not None:
@@ -79,7 +79,7 @@ class Mutation_Methods:
             def swap_genes(ga, chromosome):
                 """Swaps two random genes in the chromosome."""
 
-                index_one = random.randint(0, len(chromosome)-1)
-                index_two = random.randint(0, len(chromosome)-1)
+                index_one = random.randrange(len(chromosome))
+                index_two = random.randrange(len(chromosome))
 
                 chromosome[index_one], chromosome[index_two] = chromosome[index_two], chromosome[index_one]
diff --git a/src/parent_selection/parent_selection_methods.py b/src/parent_selection/parent_selection_methods.py
index 21891b4..2434da9 100644
--- a/src/parent_selection/parent_selection_methods.py
+++ b/src/parent_selection/parent_selection_methods.py
@@ -68,7 +68,7 @@ class Parent_Selection:
             while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
 
                 # Generate a random tournament group and sort by fitness.
-                tournament_group = sorted([random.randint(0, len(ga.population)-1) for n in range(tournament_size)])
+                tournament_group = sorted([random.randrange(len(ga.population)) for _ in range(tournament_size)])
 
                 # For each chromosome, add it to the mating pool based on its rank in the tournament.
                 for index in range(tournament_size):
@@ -78,7 +78,7 @@ class Parent_Selection:
                     #   second ranked fitness has probability: selection_probability * (1-selection_probability)
                     #   third  ranked fitness has probability: selection_probability * (1-selection_probability)^2
                     # etc.
-                    if random.uniform(0, 1) < ga.selection_probability * pow(1-ga.selection_probability, index):
+                    if random.random() < ga.selection_probability * pow(1-ga.selection_probability, index):
                         ga.population.set_parent(tournament_group[index])
 
                         # Stop if parent ratio reached
@@ -141,7 +141,7 @@ class Parent_Selection:
             while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
 
                 # Selected chromosome
-                index = random.randint(0, len(ga.population)-1)
+                index = random.randrange(len(ga.population))
 
                 # Probability of becoming a parent is fitness/max_fitness
                 if random.uniform(ga.selection_probability, 1) < ga.get_chromosome_fitness(index)/max_fitness: