Hyper-selection in dynamic environments.

In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for genetic algorithms to adapt to a new environment. This paper investigates the effect of the selection pressure on the performance of genetic algorithms in dynamic environments. A hyper-selection scheme is proposed for genetic algorithms, where the selection pressure is temporarily raised whenever the environment changes. The hyper-selection scheme can be combined with other approaches for genetic algorithms in dynamic environments. Experiments are carried out to investigate the effect of different selection pressures on the performance of genetic algorithms in dynamic environments and to investigate the effect of the hyper-selection scheme on the performance of genetic algorithms in combination with several other schemes in dynamic environments. The experimental results indicate that the effect of the hyper-selection scheme depends on the problem under consideration and other schemes combined in genetic algorithms.