diff --git a/ABC/PrefrencesABC.py b/ABC/PrefrencesABC.py
index 1641f22..db7a9f6 100644
--- a/ABC/PrefrencesABC.py
+++ b/ABC/PrefrencesABC.py
@@ -54,7 +54,14 @@ def calculate_diversity(groups: List[List[Student]]) -> float:
     preference_score = 0
 
     for group in groups:
-        scores = [student.get_score() for student in group]
+        scores = []
+        if group[0].experiment:
+            for i, student1 in enumerate(group):
+                for student2 in group[i+1:]:
+                    scores.append(student1.get_score(student2))
+        else:
+            scores = [student.get_score() for student in group]
+        
         if len(scores) > 1:  # סטיית תקן מוגדרת רק עבור יותר מנתון אחד
             diversity = statistics.stdev(scores)
         else:
@@ -173,7 +180,6 @@ def abc_algorithm_with_prefrences(students: List[Student], num_groups: int, num_
         if best_fitness > GLOBAL_MAX_VAL:
             GLOBAL_MAX_VAL = best_fitness
             GLOBAL_MAX = solutions[scores.index(GLOBAL_MAX_VAL)]
-        print(f"Iteration {iteration + 1}, Best Fitness: {GLOBAL_MAX_VAL}")
 
     # בסוף, מחזירים את הפתרון הטוב ביותר
     return GLOBAL_MAX
diff --git a/ABC/StandardABC.py b/ABC/StandardABC.py
index 766e34b..a47f885 100644
--- a/ABC/StandardABC.py
+++ b/ABC/StandardABC.py
@@ -131,9 +131,6 @@ def abc_algorithm(students: List[Student], num_groups: int, num_iterations: int
                 solutions[i] = new_sol
                 scores[i] = calculate_diversity(new_sol)
                 stagnation[i] = 0
-        # הדפסת מידע על הדור
-        best_fitness = max(scores)
-        print(f"Iteration {iteration + 1}, Best Fitness: {best_fitness}")
 
     # בסוף, מחזירים את הפתרון הטוב ביותר
     best_index = scores.index(max(scores))
diff --git a/Genetic/PreferencesGenetic.py b/Genetic/PreferencesGenetic.py
index afc91ec..7b311a5 100644
--- a/Genetic/PreferencesGenetic.py
+++ b/Genetic/PreferencesGenetic.py
@@ -53,7 +53,14 @@ def calculate_diversity(groups: List[List[Student]]) -> float:
     preference_score = 0
 
     for group in groups:
-        scores = [student.get_score() for student in group]
+        scores = []
+        if group[0].experiment:
+            for i, student1 in enumerate(group):
+                for student2 in group[i+1:]:
+                    scores.append(student1.get_score(student2))
+        else:
+            scores = [student.get_score() for student in group]
+        
         if len(scores) > 1:  # סטיית תקן מוגדרת רק עבור יותר מנתון אחד
             diversity = statistics.stdev(scores)
         else:
@@ -188,10 +195,6 @@ def genetic_algorithm_with_preferences(students: List[Student], num_groups: int,
         # עדכון האוכלוסייה
         update_population(population, fitness_scores, mutated_child)
 
-        # הדפסת מידע על הדור
-        #best_fitness = max(fitness_scores)
-        #print(f"Generation {generation + 1}, Best Fitness: {best_fitness}")
-
     # מחזירים את הפתרון הטוב ביותר
     best_index = fitness_scores.index(max(fitness_scores))
     return population[best_index]
diff --git a/Genetic/StandardGenetic.py b/Genetic/StandardGenetic.py
index 13b4d5b..149b8d7 100644
--- a/Genetic/StandardGenetic.py
+++ b/Genetic/StandardGenetic.py
@@ -151,10 +151,6 @@ def genetic_algorithm(students: List[Student], num_groups: int, population_size:
         # עדכון האוכלוסייה
         update_population(population, fitness_scores, mutated_child)
 
-        # הדפסת מידע על הדור
-        best_fitness = max(fitness_scores)
-        print(f"Generation {generation + 1}, Best Fitness: {best_fitness}")
-
     # מחזירים את הפתרון הטוב ביותר
     best_index = fitness_scores.index(max(fitness_scores))
     return population[best_index]
diff --git a/experiments/ABC/Iterations_Fitness.csv b/experiments/ABC/Iterations_Fitness.csv
new file mode 100644
index 0000000..e09ccd5
--- /dev/null
+++ b/experiments/ABC/Iterations_Fitness.csv
@@ -0,0 +1,21 @@
+Iteration,AVG Score
+1,88.7211497181652
+2,88.7211497181652
+3,89.1052713126468
+4,89.22915077725476
+5,89.25699984324228
+6,89.53983026739552
+7,89.75942600900592
+8,90.19848966650157
+9,90.19848966650157
+10,90.19848966650157
+11,90.19848966650157
+12,90.28018097627236
+13,90.53506593084174
+14,90.63147377849803
+15,90.63147377849803
+16,90.63147377849803
+17,90.63147377849803
+18,90.790154029262
+19,90.95219584823126
+20,90.95443689370146
diff --git a/experiments/ABC/Iterations_Time.csv b/experiments/ABC/Iterations_Time.csv
new file mode 100644
index 0000000..e8b39b7
--- /dev/null
+++ b/experiments/ABC/Iterations_Time.csv
@@ -0,0 +1,21 @@
+Iteration,AVG Time
+1,0.006540608406066894
+2,0.010796785354614258
+3,0.01384124755859375
+4,0.017898941040039064
+5,0.021931099891662597
+6,0.026218819618225097
+7,0.031563854217529295
+8,0.03766334056854248
+9,0.037829041481018066
+10,0.04230003356933594
+11,0.04445006847381592
+12,0.050631237030029294
+13,0.05401666164398193
+14,0.05841057300567627
+15,0.06447982788085938
+16,0.06548664569854737
+17,0.06842453479766845
+18,0.0749204158782959
+19,0.07750654220581055
+20,0.07419106960296631
diff --git a/experiments/data/RanInt_n060_ss_01.txt b/experiments/data/RanInt_n060_ss_01.txt
new file mode 100644
index 0000000..70181a1
--- /dev/null
+++ b/experiments/data/RanInt_n060_ss_01.txt
@@ -0,0 +1,1771 @@
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diff --git a/experiments/data/acceptance60_1.txt b/experiments/data/acceptance60_1.txt
new file mode 100644
index 0000000..342b465
--- /dev/null
+++ b/experiments/data/acceptance60_1.txt
@@ -0,0 +1,60 @@
+0	37	26	29	47	25	13	18	53	2	33	
+1	48	30	22	20	9	49	41	12	33	27	
+2	17	32	52	58	27	35	18	10	7	55	
+3	41	31	8	22	5	25	19	4	14	18	
+4	1	57	47	30	2	15	50	39	36	14	
+5	1	22	28	35	47	26	20	53	18	9	
+6	44	29	31	19	57	12	15	26	51	48	
+7	14	6	10	23	18	37	39	50	32	3	
+8	58	5	28	20	33	38	48	19	34	44	
+9	1	13	50	14	55	11	15	12	3	0	
+10	15	13	21	34	45	6	20	33	26	54	
+11	45	15	22	14	35	21	46	5	58	30	
+12	5	13	31	25	0	37	29	53	1	49	
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+50	6	43	10	29	56	11	21	54	27	36	
+51	38	23	31	43	10	41	28	40	37	42	
+52	49	28	14	58	45	47	11	41	29	3	
+53	6	55	49	39	7	17	16	59	9	35	
+54	12	0	3	1	53	26	56	7	45	29	
+55	1	54	56	31	36	7	52	12	25	9	
+56	49	18	40	45	24	37	25	55	12	4	
+57	18	30	19	32	6	1	7	28	2	38	
+58	25	30	42	39	38	14	15	24	0	52	
+59	56	39	45	5	18	41	13	3	1	0	
diff --git a/main.py b/main.py
index 322615e..7fea115 100644
--- a/main.py
+++ b/main.py
@@ -1,16 +1,17 @@
-from utils.helperFunctions import generate_random_student_list, generate_students_json, print_students_table, run_generations_experiment, run_mutation_experiment, run_mutation_timing_experiment, run_population_experiment, run_population_timing_experiment, run_timing_experiment, translate_file_to_students
+from utils.helperFunctions import generate_random_student_list, generate_students_json, print_students_table, run_generations_experiment, run_mutation_experiment, run_mutation_timing_experiment, run_population_experiment, run_population_timing_experiment, run_timing_experiment, translate_file_to_students, parse_problem_files
 from Genetic.StandardGenetic import genetic_algorithm
 from Genetic.PreferencesGenetic import genetic_algorithm_with_preferences
 from ABC.StandardABC import abc_algorithm
 from ABC.PrefrencesABC import abc_algorithm_with_prefrences
+import utils.experimentsFunctions as experiments
+from termcolor import colored
 
 
 def get_filename(num_students: int, num_criteria: int):
     return f"samples\students({num_students})_criteria({num_criteria}).json"
 
-def test_standard_genetic(students):
+def test_standard_genetic(students, num_groups):
     # פרמטרים
-    num_groups = 3
     population_size = 5
     generations = 50
     mutation_rate = 0.3
@@ -19,51 +20,58 @@ def test_standard_genetic(students):
     best_solution = genetic_algorithm(students, num_groups, population_size, generations, mutation_rate)
 
     # הדפסת הפתרון הטוב ביותר
-    print("\nBest Solution:")
-    for i, group in enumerate(best_solution, 1):
-        print(f"Group {i}: {group}")
+    print_solution_with_highlights(best_solution)
 
-def test_preferences_genetic(students):
+def test_preferences_genetic(students, num_groups):
     # פרמטרים
-    num_groups = 3
     population_size = 5
-    generations = 50
+    generations = 200
     mutation_rate = 0.3
 
     # הרצה
     best_solution = genetic_algorithm_with_preferences(students, num_groups, population_size, generations, mutation_rate)
 
     # הדפסת הפתרון הטוב ביותר
-    print("\nBest Solution:")
-    for i, group in enumerate(best_solution, 1):
-        print(f"Group {i} ({len(group)}): {group}")
+    print_solution_with_highlights(best_solution)
 
-def test_standard_abc(students):
+def test_standard_abc(students, num_groups):
     # פרמטרים
-    num_groups = 3
     num_iterations = 50
     limit = 10
     # הרצה
     best_solution = abc_algorithm(students, num_groups, num_iterations, limit)
 
     # הדפסת הפתרון הטוב ביותר
-    print("\nBest Solution:")
-    for i, group in enumerate(best_solution, 1):
-        print(f"Group {i}: {group}")
+    print_solution_with_highlights(best_solution)
 
-def test_prefrences_abc(students):
+def test_prefrences_abc(students, num_groups):
     # פרמטרים
-    num_groups = 3
     num_iterations = 50
     limit = 10
     # הרצה
     best_solution = abc_algorithm_with_prefrences(students, num_groups, num_iterations, limit)
 
     # הדפסת הפתרון הטוב ביותר
+    print_solution_with_highlights(best_solution)
+
+def print_solution_with_highlights(solution):
+    """
+    מדפיסה את הפתרון בצורה מוארת: ירוק אם התלמיד בקבוצה עם מישהו שהוא רוצה להיות איתו, אדום אם לא.
+    """
     print("\nBest Solution:")
-    for i, group in enumerate(best_solution, 1):
-        print(f"Group {i}: {group}")
+    for i, group in enumerate(solution, 1):
+        print(f"Group {i} ({len(group)}):")
+        group_ids = [student.id for student in group]
+        for student in group:
+            in_group_with_preferences = any(pref in group_ids for pref in student.preferences)
+            if in_group_with_preferences:
+                print(colored(f"  {student.id}: {student.preferences}", "green"))
+            else:
+                print(colored(f"  {student.id}: {student.preferences}", "red"))
+
 
 if __name__ == "__main__":
-    students = translate_file_to_students(get_filename(15, 1))
-    test_prefrences_abc(students)
+    ranint_path = "RanInt_n060_ss_01.txt"
+    acceptance_path = "acceptance60_1.txt"
+    students, num_groups = parse_problem_files(ranint_path, acceptance_path)
+    experiments.experiment_ABC_iterations(students, num_groups, 10)
diff --git a/requirements.txt b/requirements.txt
index 89f7140..9980be8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1,2 @@
-tabulate
\ No newline at end of file
+tabulate
+termcolor
\ No newline at end of file
diff --git a/utils/experimentsFunctions.py b/utils/experimentsFunctions.py
new file mode 100644
index 0000000..a768c6e
--- /dev/null
+++ b/utils/experimentsFunctions.py
@@ -0,0 +1,49 @@
+import random
+import csv
+import json
+import time
+from Genetic.PreferencesGenetic import calculate_diversity, genetic_algorithm_with_preferences
+from ABC.PrefrencesABC import abc_algorithm_with_prefrences
+from utils.student import Student
+from typing import List, Tuple
+from tabulate import tabulate
+
+def experiment_ABC_iterations(students: List[Student], num_groups: int, limit: int):
+    output_file = "ABC/Iterations"
+    # ציר X
+    iterations = list(range(1, 1001))
+    # ציר Y
+    results_fitness = []
+    results_time = []
+
+    for iteration in iterations:
+        print(f"Running ABC on {iteration} iterations...")
+        score_sum = 0
+        time_sum = 0
+        for _ in range(10):
+            start_time = time.time()
+            result = abc_algorithm_with_prefrences(students, num_groups, iteration, limit)
+            elapsed_time = time.time() - start_time
+            time_sum += elapsed_time
+            score = calculate_diversity(result)
+            score_sum += score
+        results_fitness.append((score_sum/10))
+        results_time.append((time_sum/10))
+    
+    with open(f"experiments/{output_file}_Fitness.csv", mode="w", newline='') as file:
+        fieldnames = ["Iteration", "AVG Score"]
+        writer = csv.writer(file)
+        writer.writerow(fieldnames)
+        writer.writerows(zip(iterations, results_fitness))
+
+    print(f"Experiment completed! Results saved to {output_file}_Fitness.csv")
+
+    with open(f"experiments/{output_file}_Time.csv", mode="w", newline='') as file:
+        fieldnames = ["Iteration", "AVG Time"]
+        writer = csv.writer(file)
+        writer.writerow(fieldnames)
+        writer.writerows(zip(iterations, results_time))
+
+    print(f"Experiment completed! Results saved to {output_file}_Time.csv")
+
+    
\ No newline at end of file
diff --git a/utils/helperFunctions.py b/utils/helperFunctions.py
index 9e244a4..25c4f19 100644
--- a/utils/helperFunctions.py
+++ b/utils/helperFunctions.py
@@ -4,7 +4,7 @@
 import time
 from Genetic.PreferencesGenetic import calculate_diversity, genetic_algorithm_with_preferences
 from utils.student import Student
-from typing import List
+from typing import List, Tuple
 from tabulate import tabulate
 
 def generate_random_student_list(amount: int, criteria: List[dict]) -> List[Student]:
@@ -131,6 +131,49 @@ def translate_file_to_students(file_name: str) -> List[Student]:
         print(f"קרתה שגיאה לא יודעה: {e}")
         return []
     
+def parse_problem_files(ranint_path: str, acceptance_path: str) -> Tuple[List[Student], int]:
+    """
+    פונקציה שמעבדת את זוג הקבצים RanInt ו-acceptance ומחזירה את רשימת הסטודנטים וכמות הקבוצות.
+
+    :param ranint_path: הנתיב לקובץ RanInt.
+    :param acceptance_path: הנתיב לקובץ acceptance.
+    :return: זוג (רשימת סטודנטים, מספר קבוצות).
+    """
+    # קריאת קובץ RanInt
+    with open('experiments/data/' + ranint_path, 'r') as f:
+        lines = f.readlines()
+    
+    first_line = lines[0].strip().split()
+    num_students = int(first_line[0])
+    num_groups = int(first_line[1])
+
+    criteria = {i: {} for i in range(num_students)}
+    for line in lines[1:]:
+        student1, student2, score = map(int, line.strip().split())
+        if student2 not in criteria[student1]:
+            criteria[student1][student2] = score
+        if student1 not in criteria[student2]:
+            criteria[student2][student1] = score
+
+    # קריאת קובץ acceptance
+    with open('experiments/data/' + acceptance_path, 'r') as f:
+        acceptance_lines = f.readlines()
+
+    # יצירת אובייקטים של סטודנטים
+    students = []
+    for line in acceptance_lines:
+        student_id = int(line.strip().split()[0])
+        preferences = list(map(int, line.strip().split()[1:]))
+        data = {
+            "id": student_id,
+            "name": f'Student_{student_id}',
+            "preferences": preferences,
+            "criteria": criteria[student_id]
+        }
+        students.append(Student(data, experiment = True))
+
+    return students, num_groups
+
 def run_generations_experiment(students: List[Student], num_groups: int, population_size: int, mutation_rate: float, output_file: str):
     generations_to_test = range(10, 501, 10)
     results = []
diff --git a/utils/student.py b/utils/student.py
index 3549045..e00a1b6 100644
--- a/utils/student.py
+++ b/utils/student.py
@@ -2,7 +2,7 @@ class Student:
     """
     מחלקה לייצוג סטודנט, הכוללת נתונים כמו מזהה, שם, העדפות וקריטריונים לחישוב ציון.
     """
-    def __init__(self, student_data: dict):
+    def __init__(self, student_data: dict, experiment: bool = False):
         """
         אתחול אובייקט סטודנט.
 
@@ -14,29 +14,36 @@ def __init__(self, student_data: dict):
         """
         self.id = student_data.get("id")
         self.name = student_data.get("name")
-        self.preferences = student_data.get("preferences", [])  
-        self.criteria = student_data.get("criteria", [])
+        self.preferences = student_data.get("preferences", [])
+        self.criteria = student_data.get("criteria", {} if experiment else [])
+        self.experiment = experiment
 
-    def get_score(self) -> float:
+    def get_score(self, student = None) -> float:
         """
         מחשב את הציון הכולל של הסטודנט על בסיס הקריטריונים.
 
         :return: הציון הכולל (float).
         """
-        total_score = 0.0
-        for criteria in self.criteria:
-            criteria_type = criteria.get("type")
-            value = float(criteria.get("value", 0))
+        if self.experiment:
+            if student:
+                return self.criteria[student.id]
+            else:
+                return 0
+        else:
+            total_score = 0.0
+            for criteria in self.criteria:
+                criteria_type = criteria.get("type")
+                value = float(criteria.get("value", 0))
 
-            # חישוב הציון לפי סוג הקריטריון
-            if criteria_type == "0-1":
-                total_score += value * 100
-            elif criteria_type == "0-10":
-                total_score += value * 10
-            elif criteria_type == "0-100":
-                total_score += value
+                # חישוב הציון לפי סוג הקריטריון
+                if criteria_type == "0-1":
+                    total_score += value * 100
+                elif criteria_type == "0-10":
+                    total_score += value * 10
+                elif criteria_type == "0-100":
+                    total_score += value
 
-        return total_score
+            return total_score
 
     def __repr__(self):
         """