Updated GA attribute structure, separated selection file structure

Updated GA attribute structure, separated selection file structure

src/survivor_selection/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT function_name
-from .methods import Survivor_methods
+from .survivor_selection import Survivor_Selection

src/survivor_selection/methods.py

@@ -1,8 +0,0 @@
-class Survivor_methods:
-    """Survivor methods defintion here"""
-
-    def elitism():
-        pass
-
-    def remove_two_worst():
-        pass

src/survivor_selection/survivor_selection.py

@@ -0,0 +1,36 @@
+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:
+        def repeated_crossover(ga, next_population): #Might be cheating? I don't know honestly - RG
+            while len(next_population.get_all_chromosomes()) < ga.population_size:
+                crossover_pool = ga.population.mating_pool
+
+                split_point = random.randint(0,ga.chromosome_length)    
+                chromosome_list = []
+                for i in range(len(crossover_pool)):
+                    if i + 1 < len(crossover_pool):
+                        new_gene_set = []
+                        parent_one = crossover_pool[i].get_genes()
+                        parent_two = crossover_pool[i+1].get_genes()
+                        new_gene_set.extend(parent_one[0:split_point])
+                        new_gene_set.extend(parent_two[split_point:])
+                        new_chromosome = create_chromosome(new_gene_set)
+                        chromosome_list.append(new_chromosome)
+                        
+                
+                for i in range(len(chromosome_list)):
+                    next_population.add_chromosome(chromosome_list[i])
+                    if len(next_population.get_all_chromosomes()) >= ga.population_size:
+                        break
+            return next_population
+    
+        def remove_two_worst(ga, next_population):
+            iterator = 0
+            while len(next_population.get_all_chromosomes()) < ga.population_size:
+                next_population.add_chromosome(ga.population.get_all_chromosomes()[iterator])
+                iterator += 1
+            return next_population