diff --git a/__pycache__/RotTable.cpython-37.pyc b/__pycache__/RotTable.cpython-37.pyc
index 0665bddc8d3fe8aeea7fabbae0d2c01f9aaae884..eda825aff2fc67047d02eda36807123f6b5899fc 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 a2a274a976ce1c2947d34c190976cd65879f2491..27f1de9630c467c4d2341a5f33c68535c5d6a139 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 81da80b77b9edfbb0a7e5a89350883ae4085f9c9..8a1874e03d214a4c3f2f9804b275af26d35d8f5e 100644
--- a/algogenetique.py
+++ b/algogenetique.py
@@ -32,7 +32,7 @@ def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
     return(best)
 
 
-main(100,100,0.1,50)
+main(100,100,0,50)
 
 
 
diff --git a/population.py b/population.py
index cc0e22dd45cd36a1ace7f327ff3938f386dc03bf..828caac54389b4296f47305d41e41a990b3b09dd 100644
--- a/population.py
+++ b/population.py
@@ -3,6 +3,7 @@ from random import *
 from individu import Individu
 from RotTable import RotTable
 from croisement import croisement_un_point, croisement_deux_points
+import copy
 
 class Population:
     def __init__(self,n):
@@ -13,6 +14,9 @@ class Population:
         """Fonction qui renvoie une nouvelle instance de population a partir d'une liste d'individus"""
         self.n = len(liste_individus)
         self.indiv = liste_individus
+        for i in range(0,self.n):
+            self.indiv[i].evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+
         return self
 
     def selection_p_best(self,p=None):
@@ -83,8 +87,6 @@ class Population:
                 newself.append(x)
             else:
                 newself.append(y)
-        for i in range(0, len(newself)):
-            print(newself[i].score)
         self = self.modifier_population(newself)
 
 
@@ -161,8 +163,8 @@ class Population:
         while len(newself)<vieille_taille:
             m=randrange(0,self.n)
             t=randrange(0,self.n)
-            x=newself[m]
-            y=newself[t]
+            x=copy.deepcopy(newself[m])
+            y=copy.deepcopy(newself[t])
             couple_enfant = enfant(x,y)
             for child in couple_enfant :
                 child.mutation(proba_mutation)