A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization

Algorithm 2

Genetic algorithm for workload optimization

	Input:
	Initial population size: x
	Consider total no. of iterations: y
	Output:
	Optimized solution(Os)
	Start
	Formulate initial population of x chromosomes, Oi (i = 1, 2, …, n)
	Initialize looping variable, i = 0
	Calculate fitness value of all chromosomes
	while (i < y)
	select pair (CH1, CH2) as per fitness value
	Implement crossover on (CH1, CH2) pair using crossover probability
	Implement mutation on offspring (Osp) using mutation probability
	Overwriting existing population with new population
	Increment looping variable i by 1
	end while
	return optimized solution (Os)
	Stop