diff --git a/lib/ultra_mastermind_imp.py b/lib/ultra_mastermind_imp.py
index e69de29..8468a09 100644
--- a/lib/ultra_mastermind_imp.py
+++ b/lib/ultra_mastermind_imp.py
@@ -0,0 +1,176 @@
+# Librairie du projet en version impératif
+
+import random
+import Levenshtein
+
+def min_i(array: list[int]) -> int:
+	min_val = array[0]
+	min_i = 0
+	for i in range(len(array)):
+		if array[i] < min_val:
+			min_val = array[i]
+			min_i = i 
+	return min_i
+
+def max_i(array: list[int]) -> int:
+	max_val = array[0]
+	max_i = 0
+	for i in range(len(array)):
+		if array[i] > max_val:
+			max_val = array[i]
+			max_i = i 
+	return max_i
+
+
+def new_population(pm, ng, n, ts, tm, alpha, fm): #  -> set(list(set(str)), str, int, int, int, float, float, float, int)
+	"""
+	fonction qui renvoie une nouvelle population
+	"""
+	population = {
+		"individuals": [new_individual() for i in range(n)],
+		"pm": pm,
+		"ng": ng,
+		"l": len(pm),
+		"n": n,
+		"ts": ts,
+		"tm": tm,
+		"alpha": alpha,
+		"fm": fm
+	}
+
+	for individual in population["individuals"]:
+		randomize(individual, len(pm))
+
+	return population
+
+def new_individual(): #  -> set(str)
+	"""
+	fonction qui renvoie un nouvel individu
+	"""
+	return {
+		"chromozome": ""
+	}
+
+def select(population) -> None:
+	"""
+	Methode qui sélectionne les meilleurs individus
+	"""
+	fitness_list = [] 
+	for individual in population["individuals"]:
+		match population["fm"]:
+			case 1:
+				fitness_list.append(fitness1(individual, population["pm"]))
+			case 2:
+				fitness_list.append(fitness2(individual, population["pm"], population["alpha"]))
+			case 3:
+				fitness_list.append(fitness3(individual, population["pm"]))
+			case _:
+				fitness_list.append(fitness1(individual, population["pm"]))
+
+	for i in range(int((1 - population["ts"]) * population["n"])):
+		least = min_i(fitness_list)
+		fitness_list.pop(least)
+		population["individuals"].pop(least)
+
+def reproduct(population) -> None:
+	"""
+	Methode qui reproduit les individus entre eux jusqu'à obtenir une population de taille N
+	"""
+	new = []
+	while len(population["individuals"]) + len(new) != population["n"]:
+		cut = random.randint(int(population["l"] / 3), int(2 * population["l"] / 3))
+		i = random.randint(0, len(population["individuals"]) - 1)
+		j = random.randint(0, len(population["individuals"]) - 1)
+		while i == j:
+			j = random.randint(0, len(population["individuals"]) - 1)
+		indivi_1 = population["individuals"][i]
+		indivi_2 = population['individuals'][j]
+		new_chromozome = indivi_1["chromozome"][:cut] + indivi_2["chromozome"][cut:]
+		child = new_individual()
+		child["chromozome"] = new_chromozome
+		new.append(child)
+	population["individuals"] += new
+
+def mutate_pop(population) -> None:
+	"""
+	Methode qui mute une partie de la population selon le taut de mutation
+	"""
+	mutated = []
+	for i in range(int(population["tm"] * population["n"])):
+		to_mutate = random.randint(0, population["n"] - 1)
+		while to_mutate in mutated:
+			to_mutate = random.randint(0, population["n"] - 1)
+		mutate(population["individuals"][to_mutate])
+		mutated.append(to_mutate)
+
+def print_best(population) -> None:
+	"""
+	Methode qui affiche le meilleur individu de la population
+	"""
+	fitness_list = []
+	for individual in population["individuals"]:
+		match population["fm"]:
+			case 1:
+				fitness_list.append(fitness1(individual, population["pm"]))
+			case 2:
+				fitness_list.append(fitness2(individual, population["pm"], population["alpha"]))
+			case 3:
+				fitness_list.append(fitness3(individual, population["pm"]))
+			case _:
+				fitness_list.append(fitness1(individual, population["pm"]))
+	print(population["individuals"][max_i(fitness_list)]["chromozome"])
+
+def run(population) -> None:
+	"""
+	Boucle principale
+	"""
+	for i in range(population["ng"]):
+		select(population)
+		reproduct(population)
+		mutate_pop(population)
+	print_best(population)
+
+def randomize(individual, l) -> str:
+	"""
+	Methode qui change la valeur d'un chromozome pour une valeur aléatoire
+	"""
+	new = ""
+	for i in range(l):
+		new += chr(random.randint(0, 255))
+	individual["chromozome"] = new
+
+def fitness1(individual, pm) -> int:
+	"""
+	Première methode de fitness, fait la somme des différences entre les codages des caractères des deux chaînes.
+	"""
+	sum = 0
+	for i in range(len(individual["chromozome"])):
+		sum += abs(ord(individual["chromozome"][i]) - ord(pm[i]))
+	return -sum
+
+def fitness2(individual, pm, alpha) -> int:
+	"""
+	Deuxième methode de fitness qui compte les caractères bien placés et mal placés et qui renvoie un int pondéré par alpha
+	"""
+	match = 0
+	missed_placed = 0
+	for i in range(len(individual["chromozome"])):
+		if individual["chromozome"][i] == pm[i]:
+			match += 1
+		else:
+			missed_placed += 1
+	return match + alpha * missed_placed
+
+def fitness3(individual, pm) -> int:
+	"""
+	Troisième methode de fitness qui utilise la distance de Levenshtein
+	"""
+	return -Levenshtein.distance(individual["chromozome"], pm)
+
+def mutate(individual) -> None:
+	"""
+	Methode qui change un des caractères du chromozome
+	"""
+	new = list(individual["chromozome"])
+	new[random.randint(0, len(new) - 1)] = chr(random.randint(0, 255))
+	individual["chromozome"] = "".join(new)
diff --git a/main.py b/main.py
index 15d8142..552c25b 100644
--- a/main.py
+++ b/main.py
@@ -2,21 +2,24 @@
 
 # project libs importations
 import lib.ultra_mastermind_obj as libobj
-import lib.ultra_mastermind_imp as libomp
+import lib.ultra_mastermind_imp as libimp
 
 # constants 
 PM = "Hello, world!"
-NG = 1000
+NG = 2000
 N  = 400
 TS = 0.5
 TM = 0.01
 ALPHA = 0.5
-FITNESS_METHOD = 3
+FITNESS_METHOD = 1
 
 # main function
 def main() -> None:
     pop = libobj.Population(pm = PM, ng = NG, n = N, ts = TS, tm = TM, alpha = ALPHA, fm = FITNESS_METHOD)
     pop.run()
 
+    pop = libimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
+    libimp.run(pop)
+
 if __name__ == "__main__":
 	main()