Genetic improvement (GI) uses automated search to improve existing software.
It has been successfully used to fix software bugs or improve non-functional properties of software such as running time, memory usage, or energy consumption.
Recently, it has been shown that genetic programming, the eponymous GI typical search algorithm, was outperformed by local search strategies.
One result of that work was that GI was able to find interesting algorithmic changes in the implementation of two state-of-the-art evolutionary algorithms, MOEA-D and NSGA-II.
Here, we reproduce and extend this result with a simple local search, obtaining 10% faster software variants with little to no impact on solution quality in 6/18 GI runs.