def binary_to_float(self, chromosome):

		float_representation = []
		dg = self.range[0]
		gg = self.range[1]
		for binary in chromosome:
			b = 0
			for i in range(self.numOfBits):
				b += binary[i] * math.pow(2,i)

			x = dg + (float) (b / (math.pow(2 ,self.numOfBits) - 1))*(gg-dg)

			float_representation.append(x)
		return np.array(float_representation)


	def binary_crossover(self,p1, p2):
		new = []
		for a,b in zip(p1,p2):
			r = np.random.randint(2, size=self.numOfBits)

			x = np.bitwise_or(np.bitwise_and(a,b), np.bitwise_and(r,np.bitwise_xor(a,b)))

			new.append(x)
		return new


	def binary_mutation(self, chromosome):
		mutated = []
		for bin_vec in chromosome:
			mutated_bin_vec = len(bin_vec) * [0]

			for i in range(len(bin_vec)):

				if random.random() < self.p:
					mutated_bin_vec[i] = 1 if bin_vec[i] == 0 else 0
				else:
					mutated_bin_vec[i] = bin_vec[i]

			mutated.append(np.array(mutated_bin_vec))
		return mutated