Comparison of steady state and generational genetic algorithms for use in nonstationary environments

The objective of this study is a comparison of two models of the genetic algorithm, the generational and incremental/steady state genetic algorithms, for use in nonstationary/dynamic environments. It is experimentally shown that the choice of a suitable version of the genetic algorithm can improve its performance in such environments. This can extend the ability of the genetic algorithm to track environmental changes which are relatively small and occur with low frequency without the need to implement an additional technique for tracking changing optima