Triggered hypermutation revisited

With the emergence of standardized problem generators for dynamic problem environments, we are just starting to systematically measure the performance of different evolutionary-algorithm (EA) extensions against standard classes of problems. We revisit triggered hypermutation, one of the early and most successful implementations of EA's for dynamic environments. Using an implementation of this algorithm, we systematically evaluate the performance of triggered hypermutation on specific test problems across a range of values for the environmental change rate relative to the EA "time" measured in generations. We examine the results, identify a probable cause for the algorithm's behavior, and suggest some improvements to the algorithm.