  While Genetic Improvement (GI) is a useful paradigm to improve functional and
nonfunctional aspects of software, existing techniques tended to use the same
set of mutation operators for differing objectives, due to the difficulty of
writing custom mutation operators. In this work, we suggest that Large Language
Models (LLMs) can be used to generate objective-tailored mutants, expanding the
possibilities of software optimizations that GI can perform. We further argue
that LLMs and the GI process can benefit from the strengths of one another, and
present a simple example demonstrating that LLMs can both improve the
effectiveness of the GI optimization process, while also benefiting from the
evaluation steps of GI. As a result, we believe that the combination of LLMs
and GI has the capability to significantly aid developers in optimizing their
software.
