diff --git a/src/EasyGA.py b/src/EasyGA.py
index 1ae5d60..d7a8842 100644
--- a/src/EasyGA.py
+++ b/src/EasyGA.py
@@ -23,7 +23,7 @@ class GA:
         # Termination varibles
         self.current_generation = 0
         self.current_fitness = 0
-        self.generation_goal = 3
+        self.generation_goal = 1
         self.fitness_goal = 3
         # Mutation variables
         self.mutation_rate = 0.03
@@ -84,7 +84,7 @@ class GA:
             # First get the fitness of the population
             self.get_population_fitness(self.population.chromosomes)
             # Selection - Triggers flags in the chromosome if its been selected
-            # self.selection_impl(self)
+            self.selection_impl(self)
             # Crossover - Takes the flagged chromosomes and crosses there genetic
             # makup to make new offsprings.
             # self.crossover_impl(self)
diff --git a/src/initialization/population_structure/population.py b/src/initialization/population_structure/population.py
index 18c7e38..4519585 100644
--- a/src/initialization/population_structure/population.py
+++ b/src/initialization/population_structure/population.py
@@ -39,7 +39,7 @@ class population:
         self.chromosomes[index] = chromosome
 
     def set_fitness(self, fitness):
-        """Sets the fitness value of the population""" 
+        """Sets the fitness value of the population"""
         self.fitness = fitness
 
     def __repr__(self):
@@ -53,4 +53,5 @@ class population:
         print("Current population:")
         for index in range(len(self.chromosomes)):
             print(f'Chromosome - {index} {self.chromosomes[index]}', end = "")
-            print(f' / Fitness = {self.chromosomes[index].fitness}')
+            print(f' / Fitness = {self.chromosomes[index].fitness}', end = "")
+            print(f' / Selected = {self.chromosomes[index].selected}')
diff --git a/src/selection/examples.py b/src/selection/examples.py
index 2c149a7..29f7b52 100644
--- a/src/selection/examples.py
+++ b/src/selection/examples.py
@@ -1,11 +1,59 @@
 class selection_examples:
     """Selection defintion here"""
 
-    def tournament_selection():
-        """ """
+    """Tournament selection involves running several "tournaments" among a
+    few individuals (or "chromosomes")chosen at random from the population.
+    The winner of each tournament (the one with the best fitness) is selected
+    for crossover.
+
+    Ex
+    Chromsome 1----1 wins ------
+    Chromsome 2----            - --1 wins----
+                               -            -
+    Chromsome 3----3 wins ------            -- 5 Wins --->Chromosome 5 becomes Parent
+    Chromsome 4----                         -
+                                            -
+    Chromsome 5----5 wins ---------5 wins----
+    Chromsome 6----
+   ^--Matchs--^
+    """
+
+    def match_based_tournament(ga, matchs):
+        # User selects how many matchs they want to start with to create one parent.
+
+        # Select two random parents
+        selected_1_index = random.randint(0, len(ga.population.chromosomes) - 1)]
+        selected_2_index = random.randint(0, len(ga.population.chromosomes) - 1)]
+
+        if selected_1_index <= selected_2_index:
+            return parent1
+        else:
+            return parent2
+
+    def small_tournament(ga):
+        """ Small tournament is only one round of tournament. Beat the other
+        randomly selected chromosome and your are selected as a parent.
+
+        Chromosome 1----
+                        - 1 wins -> Becomes selected for crossover.
+        Chromosome 2----            -
+        """
+        # Select two random parents
+
+        selected_1_index = random.randint(0, len(ga.population.chromosomes) - 1)]
+        selected_2_index = random.randint(0, len(ga.population.chromosomes) - 1)]
+
+        if selected_1 <= fitness2:
+            return parent1
+        else:
+            return parent2
+
+    def population_based_tournament(ga):
+        """The size of the population dictates how many matchs there will be. The
+        greater the size of the population the more matchs there is."""
         pass
 
-    def roulette_selection():
+    def roulette(ga):
         """Roulette selection works based off of how strong the fitness is of the
         chromosomes in the population. The stronger the fitness the higher the probability
         that it will be selected. Using the example of a casino roulette wheel.