diff --git a/__pycache__/RotTable.cpython-37.pyc b/__pycache__/RotTable.cpython-37.pyc
index 64b41c9070f8c3bcccf58f1c80d64aa9e723cf13..0665bddc8d3fe8aeea7fabbae0d2c01f9aaae884 100644
Binary files a/__pycache__/RotTable.cpython-37.pyc and b/__pycache__/RotTable.cpython-37.pyc differ
diff --git a/__pycache__/Traj3D.cpython-37.pyc b/__pycache__/Traj3D.cpython-37.pyc
index 5bcb4d8418f4525b71a8bb56bf694fb3e384bafb..a2a274a976ce1c2947d34c190976cd65879f2491 100644
Binary files a/__pycache__/Traj3D.cpython-37.pyc and b/__pycache__/Traj3D.cpython-37.pyc differ
diff --git a/algogenetique.py b/algogenetique.py
index 2dab902ae6d3fb5d5936b82b2d06d8621ccb46ff..81da80b77b9edfbb0a7e5a89350883ae4085f9c9 100644
--- a/algogenetique.py
+++ b/algogenetique.py
@@ -16,30 +16,23 @@ def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
 	brin = ''.join(lineList[1:])'''
     L=[]
     People=Population(N)
-    # afficher(People)
     for i in range(tmax):
-        print("\n \n NOUVELLE GENERATION \n \n")
+        print(i)
         max=0
         best=None
-        for individu in People.indiv:
-            individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
         People.reproduction(p = proportion, proba_mutation= pmutation)
-        # for individu in People.indiv:
-        #     individu.mutation(pmutation)
         for individu in People.indiv:
-            individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
             if individu.score>max:
                 best=individu
                 max=individu.score
-        # afficher(People)
         L.append(max)
-        #print(L)
+
     plt.plot([i for i in range(tmax)], L)
     plt.show()
     return(best)
 
 
-main(6,8,0.01,2)
+main(100,100,0.1,50)
 
 
 
diff --git a/individu.py b/individu.py
index e9fdceca287cbc231c4a535f03f8967c514fdf20..f243bfaba7e6bb7c22cc5c5eb3949ffbf0302d8f 100644
--- a/individu.py
+++ b/individu.py
@@ -10,7 +10,7 @@ class Individu():
 
     def __init__(self, table):
         self.table = table
-        self.score = None
+        self.score = self.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
     
     def evaluate(self, brin):
         traj = Traj3D()
@@ -39,6 +39,8 @@ class Individu():
 
         self.score = 1/max(list_distance)
 
+        return 1/distance
+
 
     def mutation(self, proba = P1):
         table_rotations = self.table.rot_table
diff --git a/population.py b/population.py
index 25f612d53f3e06512efb23ac15e5ed3426e9fbc7..28e1c231d035b0ab3c9133281b73855e0ee3a93b 100644
--- a/population.py
+++ b/population.py
@@ -48,8 +48,7 @@ class Population:
                 print("meilleur, individu: ", meilleur.score, individu.score)
                 meilleur = individu
         newself = [meilleur]
-        # print("\n \n \nmeilleur", meilleur.table.rot_table, "\n \nscore", meilleur.score)
-                           
+        vu=set()                        
         t=randrange(0,self.n)
         m=randrange(0,self.n)
         non_vu = [i for i in range(0, self.n)]          
@@ -137,7 +136,7 @@ class Population:
             p = (self.n)//2
         vieille_taille = self.n
         selection(p)
-        newself = list(self.indiv)
+        newself = [element for element in self.indiv]       
         while len(newself)<vieille_taille:
             m=randrange(0,self.n)
             t=randrange(0,self.n)
@@ -146,10 +145,12 @@ class Population:
             couple_enfant = enfant(x,y)
             for child in couple_enfant :
                 child.mutation(proba_mutation)
+                child.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
             newself.append(couple_enfant[0])
             newself.append(couple_enfant[1])
         self = self.modifier_population(newself)
 
+
 def afficher(popu):
     for individu in popu.indiv :
         print("\n individu \n")
@@ -168,8 +169,6 @@ def test():
 
 #test()
 
-
-