diff --git a/03_euler_gen_alg/main.py b/03_euler_gen_alg/main.py
index 66f5b45..3ead9e1 100644
--- a/03_euler_gen_alg/main.py
+++ b/03_euler_gen_alg/main.py
@@ -63,7 +63,7 @@ def eval_fitness(bin_pop_values):
         quad_error = quadratic_error(e_func, approx, 3) # the bigger the error, the worse the fitness
         
         inverse_fitness = 1 / quad_error # using inverse to find small errors easier
-        # print("Fitness: " + str(inverse_fitness)) # debugging
+        print("Fitness: " + str(inverse_fitness)) # debugging
         fitness_arr.append(inverse_fitness) # save fitness
         
     return fitness_arr
@@ -131,9 +131,9 @@ def mutate(population, mutation_rate):
 bin_pop_values = generate_random_population(POPULATION_SIZE)
 
 print("Working...")
-# iteration = 0 # debugging
+iteration = 0 # debugging
 while not np.any((np.array(fitness_arr)) > 200):  # Continue while any fitness value is > 1
-    # print("Iteration: " + str(iteration)) # debugging
+    print("Iteration: " + str(iteration)) # debugging
     
     # Evaluate fitness
     fitness_arr = eval_fitness(bin_pop_values)
@@ -157,7 +157,7 @@ while not np.any((np.array(fitness_arr)) > 200):  # Continue while any fitness v
         bin_pop_values.append(params)
     
     # time.sleep(0.5) # debugging
-    # iteration += 1 # debugging
+    iteration += 1 # debugging
 
 max_fitness_index = np.argmax(np.array(fitness_arr))
 a, b, c, d = [utils.bin_to_param(param) for param in bin_pop_values[max_fitness_index]]
diff --git a/03_euler_gen_alg/utils.py b/03_euler_gen_alg/utils.py
index f92c50a..812601a 100644
--- a/03_euler_gen_alg/utils.py
+++ b/03_euler_gen_alg/utils.py
@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
+import scipy.interpolate as interpolate
 
 def gray_to_bin(gray):
     """
@@ -22,12 +23,12 @@ def bin_to_gray(binary):
     gray = num ^ (num >> 1)  # Gray code formula: G = B ^ (B >> 1)
     return format(gray, '032b')  # Always return 32-bit string
 
-def bin_to_param(binary, q_min = 0.0, q_max = 10.0):
+def bin_to_param(binary, q_min = 0.0, q_max = 8.0):
     """
     Convert one binary string to float parameter in range [q_min, q_max]
     :returns: float 
     """
-    val = int(binary, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float
+    val = int(binary, 2) / 25.5 * q_max # conversion to 0.0 - 10.0 float
     # Scale to range [q_min, q_max]
     q = q_min + ((q_max - q_min) / (2**len(binary))) * val
     return q
@@ -38,7 +39,9 @@ def plot_graph(a, b, c, d):
     fig, ax = plt.subplots()
     y_approx = a*x**3 + b*x**2 + c*x + d
     y_exact = np.e**x
+    y_taylor = interpolate.approximate_taylor_polynomial(np.exp, 0, 3, 1)(x)
     ax.plot(x, y_approx, label='approx. func.')
+    ax.plot(x, y_taylor, label='taylor')
     ax.plot(x, y_exact, label='e^x')
     ax.set_xlim(-5, 5)
     ax.set_ylim(-1, 5)