The defined cliffs variant in dynamic environments: a case study using the shaky ladder hyperplane-defined functions

The shaky ladder hyperplane-defined functions (sl-hdfs) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. This test suite can generate arbitrary problems with similar levels of difficulty and it provides a platform for systematic controlled observations of the GA in dynamic environments. Previous work has found two factors that contribute to the GA's success on sl-hdfs: (1) short initial building blocks and (2) significantly changing the reward structure during fitness landscape changes. Therefore a test function that combines these two features should facilitate even better GA performance. This has led to the construction of a new sl-hdf variant, "Defined Cliffs," in which we combine short elementary building blocks with sharp transitions in the environment. We examine this variant with two different levels of dynamics, static and regularly changing, using four different metrics. The results show superior GA performance on the Defined Cliffs over all previous variants (Cliffs, Weight, and Smooth). Our observations and conclusions in this variant further the understanding of the GA in dynamic environments.