Deciding model of Population Size in time-constrained task scheduling

Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model in, we present a model to predict the optimal PS for the GA applied in time-constrained task scheduling, where the efficiency of GAs is more necessitated than in solving other kinds of problems. In the experimental evaluation, our deciding model can well predict the success ratio of the GA, given different population sizes.