diff --git a/src/EasyGA.py b/src/EasyGA.py
index ef9dff8..75b7986 100644
--- a/src/EasyGA.py
+++ b/src/EasyGA.py
@@ -19,6 +19,8 @@ class GA:
         self.mutation_rate = 0.03
         # Defualt EastGA implimentation structure
         self.gene_function_impl = random_gene
+        self.gene_input = []
+        self.gene_input_type = [] #What if user gives two numbers (i.e. [1,100]) but wants to pick between the two (domain)?
         # Set the GA Configuration
         self.initialization_impl = random_initialization
         #self.mutation_impl = PerGeneMutation(Mutation_rate)
@@ -28,12 +30,39 @@ class GA:
         #self.evaluation_impl = TestEvaluation()
 
 
-    def initialize(self):
+    def initialize(self, gene_input):
+        self.gene_input = gene_input
+
+        #assuming domain if string (strings can never be range)
+        if self.gene_input_type == []:
+            for x in range(len(self.gene_input)):
+                if (isinstance(self.gene_input[x], list)):
+                    for y in range(len(self.gene_input[x])):
+                        if isinstance(gene_input[x][y], str):
+                            self.gene_input_type.append("domain")
+                            break
+                        elif y == (len(self.gene_input[x]) -1):
+                            self.gene_input_type.append("range")
+                else:
+                    if isinstance(gene_input[x], str):
+                        self.gene_input_type.append("domain")
+                    else:
+                        if isinstance(gene_input[x], int):
+                            self.gene_input[x] = [self.gene_input[x], self.gene_input[x]]
+                        self.gene_input_type.append("range")
+                        
+        #If length doesn't correspond to chromosome, update here
+        while len(self.gene_input_type) != self.chromosome_length:
+            self.gene_input_type.append(self.gene_input_type[len(self.gene_input_type)-1])
+
         # Create the first population
         self.population = self.initialization_impl(
         self.population_size,
         self.chromosome_length,
-        self.gene_function_impl)
+        self.gene_function_impl,
+        self.gene_input,
+        self.gene_input_type)
+
 
     def evolve():
         # If you just want to evolve through all generations
diff --git a/src/initialization/gene_creation/gene_random.py b/src/initialization/gene_creation/gene_random.py
new file mode 100644
index 0000000..dbcc874
--- /dev/null
+++ b/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)
diff --git a/src/initialization/gene_function/gene_random.py b/src/initialization/gene_function/gene_random.py
index dbcc874..d3e16dd 100644
--- a/src/initialization/gene_function/gene_random.py
+++ b/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
diff --git a/src/initialization/random_initialization.py b/src/initialization/random_initialization.py
index cdef3ef..72cfbfa 100644
--- a/src/initialization/random_initialization.py
+++ b/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
\ No newline at end of file
diff --git a/src/new_initialization_method_testing.py b/src/new_initialization_method_testing.py
new file mode 100644
index 0000000..8687dd0
--- /dev/null
+++ b/src/new_initialization_method_testing.py
@@ -0,0 +1,18 @@
+import EasyGA
+import random
+
+# Create the Genetic algorithm
+ga = EasyGA.GA()
+test_range_two = [["left", "right"],[22,88],5,[22,"up"]]
+ga.initialize(test_range_two)
+ga.population.print_all()
+
+
+
+
+#test_range_one = [1,100]
+#test_domain_one = ["left", "right", "up", "down"]
+#test_range_two = [[1,100],[0,1],[33,35],[5,6]]
+#test_domain_two = [["left", "right"], ["up", "down"], ["left", "down"], ["down", "right"]]
+
+#for index-specific bounds, do list of lists i.e. test_range = [[1, 100], [1, 25], [5, 25]]
\ No newline at end of file