diff --git a/Genetic_Algorithms.py b/Genetic_Algorithms.py
new file mode 100644
index 0000000..efddc5a
--- /dev/null
+++ b/Genetic_Algorithms.py
@@ -0,0 +1,115 @@
+import numpy as np
+import pandas as pd
+import math
+import random
+
+
+M = int(input("Enter Number of Chromosomes you want"))
+N = int(input("Enter Number of Genes"))
+
+
+population = np.random.randint(2,size=(M,N))
+print(population)
+
+#Defining a Fitness Function
+def fitness_function(X):
+    return (sum(X))
+
+fitness_values=np.ndarray(shape=(M,1))
+fitness_values.shape
+
+for i in range(M):
+    fitness_value=fitness_function(population[i])
+    fitness_values[i]=fitness_value    
+
+np.hstack((population,fitness_values))
+
+
+#Roulette Wheel implementation
+normalized_fitness_values= fitness_values/sum(fitness_values)
+
+sorted_fitness_values= sorted(normalized_fitness_values,reverse=True)
+sorted_fitness_values
+
+#Calculating Cumulative Sum
+cum_sum=np.cumsum(sorted_fitness_values)
+
+cum_sum=sorted(cum_sum,reverse=True)
+
+r= random.uniform(0,1) #Generate Random number
+
+#Selecting Parent1 and Parent2
+for i in range(M):     #N is the no of Genes):
+    if r>cum_sum[i]:
+        idx=i-1
+        break
+        
+print(cum_sum[idx])        
+print(idx)
+
+R= random.uniform(0,1) #Generate Random number
+R
+
+
+#Selecting Parent1 and Parent2
+for i in range(M):
+    if R>cum_sum[i]:
+        idx2=i-1
+        break
+print(cum_sum[idx2])
+print(idx2)
+
+#Single PointCrossOver
+parent1=population[idx, :]
+parent2=population[idx2, :]
+print(parent1,parent2)
+pt1 = random.randint(1,len(parent1)-1) 
+
+print(pt1)
+#print(parent1[ : pt1])
+#print(parent2[pt1:])
+
+child1= np.concatenate((parent1[ :pt1],parent2[pt1:]),axis=0)
+child2= np.concatenate((parent1[pt1:],parent2[: pt1]))
+print(child1,child2)
+
+
+#Double Point Crossover
+pt1 = random.randint(1,len(parent1)-1)
+#pt2 = pt1
+pt2= random.randint(1,len(parent1)-1)
+ 
+if pt2>pt1:
+    temp =pt1
+    pt1=pt2
+    pt2=temp
+
+child1 = np.concatenate((parent1[ :pt1],parent2[pt1:pt2],parent1[pt2:]),axis=0)
+child1 = np.concatenate((parent2[ :pt1],parent1[pt1:pt2],parent2[pt2:]),axis=0)
+
+#print(child1,child2)
+
+#probability of crossover
+Pc=0.5
+R1=random.uniform(0,1)
+if R1<=Pc:
+   child1=child1
+else:
+   child1=parent1
+
+
+R2=random.uniform(0,1)
+if R2<=Pc:
+   child2=child2
+else:
+   child2 = parent2
+
+#Mutation
+pm=0.7
+mutationpt=random.randint(0,N) 
+R3=random.uniform(0,1)
+if R3>=pm:
+    child1[mutationpt]=(child1[mutationpt] + 1)%2;
+    print('Mutation Occurred')
+    print(child1)
+