Further optimizations, error-checking, user-input conversions

1) The initialization now accepts "general" inputs that should apply to each gene. For example, rather than a gene input of [1,100] being interpreted to mean gene 1 hsould be 1 and gene 2 should be 100, it will apply a range of [1,100] to each gene.
2) The initialization now accepts "general" gene_input_types. For example, if the user had a set of index-dependent number values, they could just say ga.gene_input_type = "domain" and the package will propagate that across all genes in the chromosome. The user still has the option of defining the entire array or just defining a specific element if they so choose. For later commits, the general gene_input_type will have to be checked for validity; for example, a string can never be a range.
3) Fixed an issue in the ordering of the initialization function call.
4) Added comments surrounding the signfiicant changes to the initialization.
5) Added example tests to the testing file.

src/initialization/gene_function/gene_random.py

@@ -11,7 +11,7 @@ def check_values(low,high):
 
 def random_gene(gene_input, gene_input_type, gene_index):
     created_gene = None
-    #Determining if single range/domain or index-dependent
+
     if gene_input_type[gene_index] == "range":
         created_gene = random.randint(gene_input[gene_index][0], gene_input[gene_index][1])
     elif gene_input_type[gene_index] == "domain":

src/initialization/random_initialization.py

@@ -13,7 +13,7 @@ def random_initialization(chromosome_length,population_size,gene_function,gene_i
         for i in range(population_size):
             chromosome = create_chromosome()
             #Fill the Chromosome with genes
-            for j in range(chromosome_length-1):
+            for j in range(chromosome_length):
                 chromosome.add_gene(create_gene(gene_function(gene_input, gene_input_type, j)))
             population.add_chromosome(chromosome)
         return population