diff --git a/hamazaki1990/individual.py b/hamazaki1990/individual.py
index 12abfe5..626e51a 100644
--- a/hamazaki1990/individual.py
+++ b/hamazaki1990/individual.py
@@ -1,15 +1,22 @@
 class Individual:
-    def __init__(self, n):
+    def __init__(self, n, f=1.0):
         self._id = n
+        self._fitness = f
 
     def get_id(self):
         return self._id
 
+    def get_fitness(self):
+        return self._fitness
+
     def __repr__(self):
         return str(self._id)
 
 
 ind = Individual(42)
 print(ind.get_id())
-print(ind)
-print(str(ind))
+print(ind.get_fitness())
+
+ind2 = Individual(30, 0.8)
+print(ind2.get_id())
+print(ind2.get_fitness())
diff --git a/hamazaki1990/population.py b/hamazaki1990/population.py
index ac19d53..cf2e18e 100644
--- a/hamazaki1990/population.py
+++ b/hamazaki1990/population.py
@@ -2,30 +2,77 @@
 from individual import Individual
 
 
+def roulettechoice(individuals, cumsum_fitness):
+    r = random.uniform(0.0, max(cumsum_fitness))
+    for i in range(len(cumsum_fitness)):
+        if r < cumsum_fitness[i]:
+            return individuals[i]
+
+
 class Population:
-    def __init__(self, size):
-        self._inds = [Individual(x) for x in range(size)]
+    def __init__(self, n, mutantrate=0, s=0):
+        num_mutant = int(n * mutantrate)
+        mutant_inds = [Individual(x, 1 + s) for x in range(num_mutant)]
+        wild_inds = [Individual(x) for x in range(num_mutant, n)]
+        self._inds = mutant_inds + wild_inds
 
     def print_ids(self):
         print(self._inds)
 
+    def print_fitness(self):
+        fitness = [x.get_fitness() for x in self._inds]
+        print(fitness)
+
     def next_genwf(self):
-        n = len(self._inds)
-        self._inds = [self._inds[random.randrange(n)] for x in range(n)]
+        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)]
+        next_generation = []
+        for x in range(size):
+            next_generation.append(roulettechoice(self._inds, cumsum_fitness))
+        self._inds = next_generation
 
     def next_genmo(self):
-        n = len(self._inds)
-        self._inds[random.randrange(n)] = self._inds[random.randrange(n)]
+        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)
+        self._inds[i_dying] = roulettechoice(self._inds, cumsum_fitness)
+
+
+p1_1 = Population(10)
+p1_1.print_ids()
+p1_1.print_fitness()
+
+for x in range(20):
+    p1_1.next_genwf()
+    p1_1.print_ids()
+p1_1.print_fitness()
+
+p1_2 = Population(10, 0.3, 0.2)
+p1_2.print_ids()
+p1_2.print_fitness()
+
+for x in range(20):
+    p1_2.next_genwf()
+    p1_2.print_ids()
+p1_2.print_fitness()
 
 
-p1 = Population(10)
+p2_1 = Population(10)
+p2_1.print_ids()
+p2_1.print_fitness()
 
-for t in range(20):
-    p1.print_ids()
-    p1.next_genwf()
+for x in range(20):
+    p2_1.next_genmo()
+    p2_1.print_ids()
+p2_1.print_fitness()
 
-p2 = Population(10)
+p2_2 = Population(10, 0.3, 0.2)
+p2_2.print_ids()
+p2_2.print_fitness()
 
-for t in range(20):
-    p2.print_ids()
-    p2.next_genmo()
+for x in range(20):
+    p2_2.next_genmo()
+    p2_2.print_ids()
+p2_2.print_fitness()