Updated Implementation Framework

Updated to cover changes made by Dan to Master regarding general design changes

Also added remove_two_worst survivor selection method

src/initialization/initialization_methods.py

@@ -0,0 +1,32 @@
+# Import the data structure
+from .population_structure.population import Population as create_population
+from .chromosome_structure.chromosome import Chromosome as create_chromosome
+from .gene_structure.gene import Gene as create_gene
+
+class Initialization_Methods:
+    """Initialization examples that are used as defaults and examples"""
+    
+    def random_initialization(self, ga):
+        """Takes the initialization inputs and choregraphs them to output the type of population with the given parameters."""
+
+        # Create the population object
+        population = create_population()
+
+        # Fill the population with chromosomes
+        for i in range(ga.population_size):
+            chromosome = create_chromosome()
+            #Fill the Chromosome with genes
+            for j in range(ga.chromosome_length):
+                # Using the chromosome_impl to set every index inside of the chromosome
+                if ga.chromosome_impl != None:
+                    # Each chromosome location is specified with its own function
+                    chromosome.add_gene(create_gene(ga.chromosome_impl(j)))
+                    # Will break if chromosome_length != len(lists) in domain
+                elif ga.gene_impl != None:
+                    function = ga.gene_impl[0]
+                    chromosome.add_gene(create_gene(function(*ga.gene_impl[1:])))
+                else:
+                    #Exit because either were not specified
+                    print("You did not specify any initialization constraints.")
+            population.add_chromosome(chromosome)
+        return population