diff --git a/hamazaki1990/individual.py b/hamazaki1990/individual.py
index 01ad902..688b678 100644
--- a/hamazaki1990/individual.py
+++ b/hamazaki1990/individual.py
@@ -1,8 +1,9 @@
 import random
+import copy
 
 
 class Individual:
-    mutationrate = 0.001
+    mutationrate = 1
 
     def __init__(self, n, f=1.0):
         self._id = n
@@ -20,10 +21,12 @@ def get_genotype(self):
 
     def acquire_mutation(self):
         r = random.random()
-        next_genotype = self._genotype
+        next_ind = copy.deepcopy(self)
+        next_genotype = next_ind._genotype
         if r < Individual.mutationrate:
             next_genotype.append(random.random())
-        self._genotype = next_genotype
+        next_ind._genotype = next_genotype
+        return next_ind
 
     def __repr__(self):
         return str(self._id)
@@ -34,11 +37,13 @@ def main():
     print(ind.get_id())
     print(ind.get_fitness())
 
-    ind2 = Individual(30, 0.8)
+    ind2 = Individual(30)
     print(ind2.get_id())
     print(ind2.get_fitness())
-    ind2.acquire_mutation()
     print(ind2.get_genotype())
+    ind2_1 = ind2.acquire_mutation()
+    print(ind2.get_genotype())
+    print(ind2_1.get_genotype())
     ind2.acquire_mutation()
     print(ind2.get_genotype())
     print(ind2.get_id())
diff --git a/hamazaki1990/population.py b/hamazaki1990/population.py
index 462d32e..29af423 100644
--- a/hamazaki1990/population.py
+++ b/hamazaki1990/population.py
@@ -1,5 +1,4 @@
 import random
-import copy
 from individual import Individual
 
 
@@ -34,19 +33,17 @@ def next_genwf(self):
         cumsum_fitness = [sum(fitness[:i]) for i in range(1, size + 1)]
         next_generation = []
         for x in range(size):
-            current_inds = copy.deepcopy(self._inds)
-            next_inds = roulettechoice(current_inds, cumsum_fitness)
-            next_inds.acquire_mutation()
+            parent_inds = roulettechoice(self._inds, cumsum_fitness)
+            next_inds = parent_inds.acquire_mutation()
             next_generation.append(next_inds)
-        self._inds = next_generation
+        return next_generation
 
     def next_genmo(self):
         fitness = [x.get_fitness() for x in self._inds]
         size = len(self._inds)
         cumsum_fitness = [sum(fitness[:i]) for i in range(1, size + 1)]
         i_dying = random.randrange(size)
-        current_inds = copy.deepcopy(self._inds)
-        next_inds = roulettechoice(current_inds, cumsum_fitness)
+        next_inds = roulettechoice(self._inds, cumsum_fitness)
         next_inds.acquire_mutation()
         self._inds[i_dying] = next_inds