Measurements for understanding the behavior of the genetic algorithm in dynamic environments: a case study using the Shaky Ladder Hyperplane-Defined Functions

We describe a set of measures to examine the behavior of the Genetic Algorithm (GA) in dynamic environments. We describe how to use both average and best measures to look at performance, satisficability, robustness, and diversity. We use these measures to examine GA behavior with a recently devised dynamic test suite, the Shaky Ladder Hyperplane-Defined Functions (sl-hdf's). This test suite can generate random problems with similar levels of difficulty and provides a platform allowing systematic controlled observations of the GA in dynamic environments. We examine the results of these measures in two different versions of the sl-hdf's, one static and one regularly-changing. We provide explanations for the observations in these two different environments, and give suggestions as to future work.