Determining the Best Mutation Probabilities of a Genetic Algorithm for Mapping Tasks

An important aspect of heterogeneous computing systems is the problem of efficiently mapping tasks to processors. There are various methods of obtaining acceptable solutions to this problem but the genetic algorithm is considered to be among the best heuristics for assigning independent tasks to processors. This paper focuses on how the genetic heuristic can be improved by determining the best probabilities for a three-step mutation operator. By computing the probabilities for selecting a mutation combination we concluded that the most favoured combinations are the ones which select a task from the processor with the biggest total execution time and then move the selected task to the processor which executes it the fastest. Also, the probability of applying the special mutation operator on a chromosome must be much greater than the probability of applying the crossover operator.