To improve the understanding of the GA in dynamic environments we explore a set of test problems, the shaky ladder hyper-defined functions (sl-hdf), and extend these functions to create versions that are equivalent to many classical dynamic problems. We do this by constraining the space of all sl-hdfs to create representations of these classical functions. We have examined three classical problems, and compared sl-hdf versions of these problems with their standard representations. These results show that the sl-hdfs are representative of a larger class of problems, and can represent a larger class of test suite. Previous results on sl-hdf showed that GA performance is best in the Defined Cliffs variant of the sl-hdf. We build upon these results to improve GA performance in several classes of real world dynamic problems by modifying the problem representation. These results lend insight into dynamic problems where the GA will perform well.