diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..6ff5f5a98092a23fdd0b88d61529be1f32fa19b0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+*.pyc
+__pycache__/
+*.png
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 395ba2d31e295f66eef5e2bd3db334975a462037..31e7ddfdf3472dbdd1b04dd753c498bc2995fd54 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,3 +1,3 @@
 {
-    "python.pythonPath": "D:\\Programmes\\Anaconda3\\python.exe"
+    "python.pythonPath": "/Users/gauthierroy/anaconda3/bin/python"
 }
\ No newline at end of file
diff --git a/__pycache__/population.cpython-37.pyc b/__pycache__/population.cpython-37.pyc
index 0a6a330d28c49e773950856fc4d57e01af3ba167..cc5599e44a6d95a4bb0948d49a52bd4a8258716e 100644
Binary files a/__pycache__/population.cpython-37.pyc and b/__pycache__/population.cpython-37.pyc differ
diff --git a/algogenetique.py b/algogenetique.py
index 76fab257900c4312b38b442075b151debc48e374..50b8dd32560cdb65f6e8ceb7832bc5e74acacd15 100644
--- a/algogenetique.py
+++ b/algogenetique.py
@@ -10,25 +10,24 @@ from random import random
 import matplotlib.pyplot as plt
 
 
+# def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
+#     '''lineList = [line.rstrip('\n') for line in open(brin)]
+# 	brin = ''.join(lineList[1:])'''
+#     L=[]
+#     People=Population(N)
+#     for i in range(tmax):
+#         print(i)
+#         max=0
+#         best=None
+#         People.reproduction(p = proportion, proba_mutation= pmutation)
+#         for individu in People.indiv:
+#             if individu.score>max:
+#                 best=individu
+#                 max=individu.score
+#         L.append(max)
 
-def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
-    '''lineList = [line.rstrip('\n') for line in open(brin)]
-	brin = ''.join(lineList[1:])'''
-    L=[]
-    People=Population(N)
-    for i in range(tmax):
-        print(i)
-        max=0
-        best=None
-        People.reproduction(p = proportion, proba_mutation= pmutation)
-        for individu in People.indiv:
-            if individu.score>max:
-                best=individu
-                max=individu.score
-        L.append(max)
-
-    plt.plot([i for i in range(tmax)], L, label = str(pmutation))
-    return(best)
+#     plt.plot([i for i in range(tmax)], L, label = str(pmutation))
+#     return(best)
 
 def test_mutation():
     plt.figure()
@@ -40,5 +39,62 @@ def test_mutation():
     plt.ylabel("Score du meilleur individu")
     plt.title("Comparaison en fonction du taux de mutation")
     plt.show()
+    
+
 
+import time 
+
+# Debut du decompte du temps
+start_time = time.time()
 test_mutation()
+
+
+def main(N,tmax,pmutation, proportion):
+    L=[]
+    lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+    brin = ''.join(lineList[1:])
+    People=Population(N)
+    # S1=[]
+    for individu in People.indiv:
+        individu.evaluate(brin)
+        # S1.append(int(individu.score))
+    # maximum=int(max(S1))
+    for i in range(tmax):
+        print(i)
+        mini=People.indiv[0].score
+        best=People.indiv[0]
+        People.reproduction(p = proportion, proba_mutation= pmutation)
+        for individu in People.indiv:
+            if individu.score<mini:
+                best=individu
+                mini=individu.score
+        L.append(mini)
+
+    # plt.subplot(221)
+    plt.plot([i for i in range(tmax)], L, label = str(pmutation))
+    
+
+    # plt.subplot(223)
+    # plt.hist(S1, range = (0, maximum+10), bins = 20, color = 'red')
+
+    # S2=[individu.score for individu in People.indiv]
+    # print("Score final: ",best.score)
+
+
+    # plt.subplot(224)
+    # plt.hist(S2, range = (0,maximum+10), bins = 20, color = 'blue')
+    # plt.show()
+   
+
+    return(best,People)
+
+# lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+# brin = ''.join(lineList[1:])
+# best,People = main(10,10,0.01,5)
+# test = Traj3D()
+# test.compute(brin, best.table)
+# test.draw("first_plot")
+
+
+# Affichage du temps d execution
+print("Temps d'execution : %s secondes " % (time.time() - start_time))
diff --git a/individu.py b/individu.py
index 24193810b0c780eb4bc09bdd034a82094d59f376..20958d3de778f360a6da39856777dffe233336bf 100644
--- a/individu.py
+++ b/individu.py
@@ -37,9 +37,9 @@ class Individu():
                 list_distance += [distance_first_nuc, distance_last_nuc]
 
 
-        self.score = 1/max(list_distance)
+        self.score = max(list_distance)
 
-        return 1/max(list_distance)
+        return max(list_distance)
 
 
     def mutation(self, proba = P1):
diff --git a/population.py b/population.py
index 828caac54389b4296f47305d41e41a990b3b09dd..e615f16dda0e2739e637955af70bffeda42c7cf9 100644
--- a/population.py
+++ b/population.py
@@ -67,7 +67,11 @@ class Population:
             p = (self.n)//2
         meilleur = self.indiv[0]
         for individu in self.indiv :
+<<<<<<< HEAD
             if meilleur.score < individu.score:
+=======
+            if meilleur.score > individu.score:
+>>>>>>> 69e8a061d6fd93996d67f8b97e2c0e9d1e93f60a
                 print("meilleur, individu: ", meilleur.score, individu.score)
                 meilleur = individu
         newself = [meilleur]
@@ -83,7 +87,11 @@ class Population:
             
             x=self.indiv[m]
             y=self.indiv[t]
+<<<<<<< HEAD
             if x.score>=y.score:
+=======
+            if x.score<y.score:
+>>>>>>> 69e8a061d6fd93996d67f8b97e2c0e9d1e93f60a
                 newself.append(x)
             else:
                 newself.append(y)
@@ -167,8 +175,10 @@ class Population:
             y=copy.deepcopy(newself[t])
             couple_enfant = enfant(x,y)
             for child in couple_enfant :
+                lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+                brin = ''.join(lineList[1:])
                 child.mutation(proba_mutation)
-                child.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+                child.evaluate(brin)
             newself.append(couple_enfant[0])
             newself.append(couple_enfant[1])
         self = self.modifier_population(newself)