diff --git a/evolve_text.py b/evolve_text.py
old mode 100644
new mode 100755
index e0202d2..6c0d11a
--- a/evolve_text.py
+++ b/evolve_text.py
@@ -19,7 +19,6 @@
 from deap import base
 from deap import tools
 
-
 #-----------------------------------------------------------------------------
 # Global variables
 #-----------------------------------------------------------------------------
@@ -95,6 +94,19 @@ def get_text(self):
 # TODO: Implement levenshtein_distance function (see Day 9 in-class exercises)
 # HINT: Now would be a great time to implement memoization if you haven't
 
+
+def levenshtein_distance(s1,s2, mem = {}):
+    if((s1, s2) in mem.keys()):
+        return mem[(s1, s2)]
+    if len(s1) == 0:
+        return len(s2)
+    if len(s2) == 0:
+        return len(s1)
+    temp = min([int(s1[0] != s2[0]) + levenshtein_distance(s1[1:],s2[1:]), 1+levenshtein_distance(s1[1:],s2), 1+levenshtein_distance(s1,s2[1:])])
+    mem[(s1, s2)] = temp
+    return temp
+
+
 def evaluate_text(message, goal_text, verbose=VERBOSE):
     """
     Given a Message and a goal_text string, return the Levenshtein distance
@@ -107,7 +119,7 @@ def evaluate_text(message, goal_text, verbose=VERBOSE):
     return (distance, )     # Length 1 tuple, required by DEAP
 
 
-def mutate_text(message, prob_ins=0.05, prob_del=0.05, prob_sub=0.05):
+def mutate_text(message, prob_ins=0.2, prob_del=0.2, prob_sub=0.2):
     """
     Given a Message and independent probabilities for each mutation type,
     return a length 1 tuple containing the mutated Message.
@@ -119,15 +131,12 @@ def mutate_text(message, prob_ins=0.05, prob_del=0.05, prob_sub=0.05):
         Substitution:   Replace one character of the Message with a random
                         (legal) character
     """
-
     if random.random() < prob_ins:
-        # TODO: Implement insertion-type mutation
-        pass
-
-    # TODO: Also implement deletion and substitution mutations
-    # HINT: Message objects inherit from list, so they also inherit
-    #       useful list methods
-    # HINT: You probably want to use the VALID_CHARS global variable
+        message.insert(VALID_CHARS[random.randint(0, len(VALID_CHARS) - 1)], random.randint(0, len(message) - 1))
+    if random.random() < prob_del:
+        message.pop(random.randint(0, len(message) - 1))
+    if random.random() < prob_sub:
+        message[random.randint(0, len(message) - 1)] = VALID_CHARS[random.randint(0, len(VALID_CHARS) - 1)]
 
     return (message, )   # Length 1 tuple, required by DEAP
 
@@ -170,7 +179,7 @@ def evolve_string(text):
 
     # Get configured toolbox and create a population of random Messages
     toolbox = get_toolbox(text)
-    pop = toolbox.population(n=300)
+    pop = toolbox.population(n=1000)
 
     # Collect statistics as the EA runs
     stats = tools.Statistics(lambda ind: ind.fitness.values)
@@ -184,8 +193,8 @@ def evolve_string(text):
     pop, log = algorithms.eaSimple(pop,
                                    toolbox,
                                    cxpb=0.5,    # Prob. of crossover (mating)
-                                   mutpb=0.2,   # Probability of mutation
-                                   ngen=500,    # Num. of generations to run
+                                   mutpb=0.4,   # Probability of mutation
+                                   ngen=800,    # Num. of generations to run
                                    stats=stats)
 
     return pop, log
diff --git a/results.txt b/results.txt
new file mode 100644
index 0000000..ae63e04
--- /dev/null
+++ b/results.txt
@@ -0,0 +1 @@
+After running the program several times with the default string and not achieving the target message within the 500 generations, I decided to increase all rates of mutations, population size, and number of generations run.  Increasing the population size caused the program to reach the target within fewer generations, as there was a larger pool of messages from which to select from and thus a greater chance that a message close to the target will be created with each generation.  Increasing the rate of mutations was useful until a certain rate was reached, but higher rates of mutations cause the differences between generations to be too sporadic and random, and generations' offspring are less weighted towards developing to the target message.  Increasing the number of generations simply increased the number of trials run, offering more time for the target message to be reached when increased, and limiting the time when decreased.
\ No newline at end of file