A Representation for Genetic-Algorithm-Based Multiprocessor Task Scheduling

Abstract

A multiprocessor scheduling problem is defined as the assignment of a given set of tasks to a set of processors. These tasks should be assigned in a way such that the total execution time is minimized and certain criteria are met. A wide range of solutions and heuristics have been proposed to solve this important system optimization problem. In this paper, we propose a novel representation to solve the task scheduling problem using genetic algorithm (GA). This representation is novel not only in the way it presents task scheduling, but also in that the length of that representation is intelligently adaptable to the given problem. Task duplication is allowed in our method and it is capable of spanning a large proportion of the solution space without the need for penalty/rewards or adding repair mechanisms whilst always generating valid chromosomes. Due to this new representation, order of the search space has been reduced; consequently, the proposed approach outperforms some recently studied GA based scheduling methods over 120 times with respect to the number of fitness evaluations.