diff --git a/src/EasyGA.py b/src/EasyGA.py
index b920c82..912b550 100644
--- a/src/EasyGA.py
+++ b/src/EasyGA.py
@@ -1,12 +1,10 @@
 import random
-
-# Import all the data prebuilt modules
+# Import all the data structure prebuilt modules
 from initialization import population as create_population
 from initialization import chromosome as create_chromosome
 from initialization import gene as create_gene
-#
-from fitness_function import fitness_examples
 # Import example classes
+from fitness_function import fitness_examples
 from initialization import initialization_examples
 from termination_point import termination_examples
 from selection import selection_examples
@@ -24,13 +22,12 @@ class GA:
         self.population = None
         # Termination varibles
         self.current_generation = 0
-        self.max_generations = 3
         self.current_fitness = 0
-        self.goal_fitness = 3
+        self.generation_goal = 3
+        self.fitness_goal = 3
         # Mutation variables
         self.mutation_rate = 0.03
 
-
         # Defualt EastGA implimentation structure
         self.initialization_impl = initialization_examples.random_initialization
         self.fitness_funciton_impl = fitness_examples.is_it_5
@@ -39,9 +36,6 @@ class GA:
         #self.crossover_impl = FastSinglePointCrossover()
         self.termination_impl = termination_examples.generation_based
 
-        # If we want the fitnesses to be updated by the computer
-        self.update_fitness = True
-
     def initialize_population(self):
         """Initialize the population using the initialization
          implimentation that is currently set"""
diff --git a/src/termination_point/examples.py b/src/termination_point/examples.py
index 81d31f1..823757a 100644
--- a/src/termination_point/examples.py
+++ b/src/termination_point/examples.py
@@ -4,13 +4,13 @@ class termination_examples:
     def fitness_based(ga):
         """Fitness based approach to terminate when the goal fitness has been reached"""
         status = True
-        if(ga.current_fitness > ga.goal_fitness):
+        if(ga.current_fitness > ga.fitness_goal):
             status = False
         return status
 
     def generation_based(ga):
         """Generation based approach to terminate when the goal generation has been reached"""
         status = True
-        if(ga.current_generation > ga.max_generations):
+        if(ga.current_generation > ga.generation_goal):
             status = False
         return status