From 50e7166ea5b4aa803851b468358bc4137ba211f6 Mon Sep 17 00:00:00 2001
From: danielwilczak101 <44122838+danielwilczak101@users.noreply.github.com>
Date: Mon, 12 Oct 2020 23:13:02 -0400
Subject: [PATCH] Removed useless import statements

---
 .../parent_selection_methods.py               | 20 ++++++++-----------
 .../survivor_selection_methods.py             |  4 ----
 2 files changed, 8 insertions(+), 16 deletions(-)

diff --git a/src/parent_selection/parent_selection_methods.py b/src/parent_selection/parent_selection_methods.py
index 5288c72..eeb7350 100644
--- a/src/parent_selection/parent_selection_methods.py
+++ b/src/parent_selection/parent_selection_methods.py
@@ -1,8 +1,4 @@
 import random
-from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
-from initialization.gene_structure.gene import Gene as create_gene
-from initialization.population_structure.population import Population
-from initialization.chromosome_structure.chromosome import Chromosome
 
 class Parent_Selection:
 
@@ -13,18 +9,18 @@ class Parent_Selection:
             Will make tournaments of size tournament_size and choose the winner (best fitness) from the tournament and use it as a parent for the next generation
             The total number of parents selected is determined by parent_ratio, an attribute to the GA object.
             """
-            
+
             tournament_size = int(ga.population.size()*ga.tournament_size_ratio)
             if tournament_size < 5:
                 tournament_size = 5
             # Probability used for determining if a chromosome should enter the mating pool.
             selection_probability = ga.selection_probability
-            
+
             # Repeat tournaments until the mating pool is large enough.
             while (len(ga.population.mating_pool) < ga.population.size()*ga.parent_ratio):
                 # Generate a random tournament group and sort by fitness.
                 tournament_group = ga.sort_by_best_fitness([random.choice(ga.population.get_all_chromosomes()) for n 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):
                     # Probability required is selection_probability * (1-selection_probability) ^ (tournament_size-index+1)
@@ -45,19 +41,19 @@ class Parent_Selection:
             Where the chromosomes are the numbers to be selected and the board size for
             those numbers are directly proportional to the chromosome's current fitness. Where
             the ball falls is a randomly generated number between 0 and 1"""
-            
+
             total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(ga.population.size()))
             rel_fitnesses = []
-            
+
             for chromosome in ga.population.chromosome_list:
                 if (total_fitness != 0):
                     rel_fitnesses.append(float(chromosome.fitness)/total_fitness)
-            
+
             probability = [sum(rel_fitnesses[:i+1]) for i in range(len(rel_fitnesses))]
-            
+
             while (len(ga.population.mating_pool) < ga.population.size()*ga.parent_ratio):
                 rand_number = random.random()
-                
+
                 # Loop through the list of probabilities
                 for i in range(len(probability)):
                     # If the probability is greater than the random_number, then select that chromosome
diff --git a/src/survivor_selection/survivor_selection_methods.py b/src/survivor_selection/survivor_selection_methods.py
index dd22e55..c6c66ac 100644
--- a/src/survivor_selection/survivor_selection_methods.py
+++ b/src/survivor_selection/survivor_selection_methods.py
@@ -1,8 +1,4 @@
 import random
-from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
-from initialization.gene_structure.gene import Gene as create_gene
-from initialization.population_structure.population import Population
-from initialization.chromosome_structure.chromosome import Chromosome
 
 class Survivor_Selection:
     """Survivor selection determines which individuals should be brought to the next generation"""