import numpy as np

population_size= 10
size= 10
generations= 1000
F=0.5

population= np.random.rand(population_size, size) 
print(population)

def fitness(x):
	diff= 0
	for i in range(size):
		diff+= (x[i] - 77.0)*(x[i] - 77.0)
	return -diff

for i in range(generations):
	#print("generation")
	#print(i)

	for j in range(population_size):
		
	#	print("individual")
#		print(j)

		mutation= [0 for i in range(size)]

		# mutation
		for k in range(size):
			random_value= np.random.rand(1)	
			
			if random_value < 0.1:
				random_value= np.random.rand(1)	
				r1=np.random.randint(population_size)
				r2=np.random.randint(population_size)
				r3=np.random.randint(population_size)
				
				mutation[k]= population[r1][k] + F*(population[r2][k] - population[r3][k])
			else:
				mutation[k]= population[j][k]

		
	#	print(population[j])
#		print("mutation")
#		print(mutation)

		f_mutation= fitness(mutation)
		f = fitness(population[j])

		#selection
		if f_mutation > f:
			population[j]= mutation[:]
		
	f_vector= [0 for i in range(population_size)]
	for j in range(population_size):
		f_vector[j]= fitness(population[j])
	
	#print("Max Fitness")
	print(max(f_vector))

	

f_vector= [0 for i in range(population_size)]
for j in range(population_size):
	f_vector[j]= fitness(population[j])

print("Final Max Fitness")
print(max(f_vector))