Diversity Control Mechanism in Genetic Algorithm to Solve a Single Machine Scheduling Problem

Minimizing the total weighted number of tardy jobs in single machine is known to be strongly NP-hard. In this paper, genetic algorithm (GA) is used to solve this problem. Since population diversity in genetic algorithm is important for the aim of preventing neighborhood search from trapping in local optimum, different diversity control mechanisms are considered in GA. The diversity control mechanisms are designed to react to population diversity by changing the mutation rate. The goal of this paper is to find best diversity control function to encourage efficient and effective search. In order to evaluate the performance of diversity control functions, numerous runs are implemented and their convergence integrity and diversity speediness are compared.