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