Adaptive mutation in dynamic environments

The interest in nature inspired optimization in dynamic environments has been increasing constantly over the past years. This trend implies that many real world problems experience dynamic changes and it is important to develop an efficient algorithm capable of tackling these problems. Several techniques have been developed over the past two decades for solving dynamic optimization problems. Among these techniques, the hypermutation scheme has proved to be beneficial in solving some of the dynamic optimization problems but requires that the mutation factors be picked a priori. This paper investigates a new mutation and change detection scheme for compact genetic algorithm (cGA), where the degree of change regulates the mutation rate (i.e. mutation rate is directly proportional to the degree of change). The experimental results shows that the mutation and change detection scheme has an impact on the performance of the cGA in dynamic environments and that the effect of the proposed scheme depends on the dynamics of the environment.