New Initialization Method

This is a test implementation of a potential new initialization method. A testing file - new_initialization_method_testing.py - is included to allow for quick testing.

In summary here is are the major points:
1) Two new attributes of GA were created - gene_input and gene_input_type. gene_input holds the user's custom range(s)/domain(s) after it gets passed to the initialize() function. gene_input_type holds an array with the same length as the chromosomes that holds the input type of the user's gene_input on a gene-by-gene basis. It does this in the same exact way that index-dependent gene ranges/domains are handled. By making the gene_input_type array the same size as the chromosome, the elements can be paired very easily. The acceptable values for this are either "range" or "domain". With a range, any value between the two can be generated; with domain, only the two elements included can be selected from randomly.
2) As mentioned in change 1, the user now has to pass their range(s)/domain(s) to the initialize() function.
3) The package is capable of implicitly determining if a certain input from the user is a range or domain. Strings can only ever be a domain – if given an element that only includes integers, the program assumes range.
4) If the user wishes to use numbers only as a domain, they can specify this by directly interacting with the ga.gene_input_type (or through a setter function).
5) the initialize() function in the GA object determines the implicit range/domain assignments if the user doesn’t do so themselves.
6) The random_initialization() function is effectively the same, except there is now an if/else to determine if the user is using the built-in gene creation function or not. If they are, then pass the gene_input, gene_input_type, and current gene index as arguments to the gene function. If they are using their own function, random_initialization() functions exactly the same way as it does in the current master branch.
7) Based on all the settings mentioned above, the random_gene() function will create a value before passing it back to random_initialization().

src/initialization/gene_creation/gene_random.py

@@ -0,0 +1,13 @@
+# Imported library
+import random
+
+def check_values(low,high):
+    #Check to make sure its not less then zero
+    assert low > 0 , "The random gene low can not be less then zero"
+    # Check to make sure the high value is not
+    # lower than or equal to low and not 0.
+    assert high > low , "High value can not be smaller then low value"
+    assert high != 0, "High value can not be zero"
+
+def random_gene():
+    return random.randint(1,100)

src/initialization/gene_function/gene_random.py

@@ -6,8 +6,22 @@ def check_values(low,high):
     assert low > 0 , "The random gene low can not be less then zero"
     # Check to make sure the high value is not
     # lower than or equal to low and not 0.
-    assert high > low , "High value can not be smaller then low value"
+    assert high > low, "High value can not be smaller then low value"
     assert high != 0, "High value can not be zero"
 
-def random_gene():
-    return random.randint(1,100)
+def random_gene(gene_input, gene_input_type, gene_index):
+    
+    created_gene = None
+    #Determining if single range/domain or index-dependent
+    if isinstance(gene_input[0], list):
+        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":
+            created_gene = random.choice(gene_input[gene_index])
+    else:
+        if gene_input_type[gene_index] == "range":
+            created_gene = random.randint(gene_input[0], gene_input[1])
+        elif gene_input_type[gene_index] == "domain":
+            created_gene = random.choice(gene_input)
+
+    return created_gene

src/initialization/random_initialization.py

@@ -2,15 +2,30 @@
 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
+from .gene_function.gene_random import random_gene as random_gene
 
-def random_initialization(chromosome_length,population_size,gene_function):
-    # Create the population object
-    population = create_population()
-    # Fill the population with chromosomes
-    for i in range(population_size):
-        chromosome = create_chromosome()
-        #Fill the Chromosome with genes
-        for j in range(chromosome_length):
-            chromosome.add_gene(create_gene(gene_function()))
-        population.add_chromosome(chromosome)
-    return population
+def random_initialization(chromosome_length,population_size,gene_function,gene_input,gene_input_type):
+
+    if gene_function == random_gene:
+        # Create the population object
+        population = create_population()
+        # Fill the population with chromosomes
+        for i in range(population_size):
+            chromosome = create_chromosome()
+            #Fill the Chromosome with genes
+            for j in range(chromosome_length-1):
+                chromosome.add_gene(create_gene(gene_function(gene_input, gene_input_type, j)))
+            population.add_chromosome(chromosome)
+        return population
+
+    else: #For user input gene-function, don't do anything with gene_input parameter
+        # Create the population object
+        population = create_population()
+        # Fill the population with chromosomes
+        for i in range(population_size):
+            chromosome = create_chromosome()
+            #Fill the Chromosome with genes
+            for j in range(chromosome_length):
+                chromosome.add_gene(create_gene(gene_function()))
+            population.add_chromosome(chromosome)
+        return population