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/individu.py b/individu.py
index f243bfaba7e6bb7c22cc5c5eb3949ffbf0302d8f..24193810b0c780eb4bc09bdd034a82094d59f376 100644
--- a/individu.py
+++ b/individu.py
@@ -39,7 +39,7 @@ class Individu():
 
         self.score = 1/max(list_distance)
 
-        return 1/distance
+        return 1/max(list_distance)
 
 
     def mutation(self, proba = P1):
diff --git a/population.py b/population.py
index 559bebbe83e60dab6a55747e9a11f189d99dae0d..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):
@@ -13,9 +14,15 @@ 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=self.n//2):
+    def selection_p_best(self,p=None):
+        if p==None:
+            p=(self.n)//2
+            
         def tri_rapide_aux(tableau,debut,fin):
             if debut < fin-1:
                 positionPivot=partitionner(tableau,debut,fin)
@@ -80,8 +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)
 
 
@@ -158,8 +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)